Reviewerly/exHarmony huggingface dataset:
A New Hybrid Approach for Data Clustering Analysis using Hybrid Fuzzy C-Means and Fuzzy Particle Swarm Optimization

The most reliable and robust document clustering algorithms have played a significant role in document clustering for active celestial navigation, summarize, and organization of data. K-means, a common partitioned clustering algorithm, has the potential to fix powerful mathematical tasks in a short period of time.  In this paper, an effective document clustering process is performed using a new hybridized k-means based genetic algorithm. Thereby, this new hybridized algorithm has prevented the solutions trapping inside the local optima. By the fact, the better solutions generated using this hybridized algorithm tackles clustering issues during cluster formation with a huge number of documents/data. The hybrid algorithms once equated with traditional methods on two benchmark text document datasets afford improved superiority document clusters in terms of two standard document clustering assessment measures Entropy and F-Measure. Experimental validation was conducted on four data set categories. Experimental outcomes have proved the effectiveness of proposed hybridized algorithms compared to other conventional optimization algorithms such as, PSO and K-means in terms of clustering efficiency, accuracy, and F-measure.
Teaching C++ as a First Programming Language

Nowadays, most C++ introductory courses begin by studying the procedural constructs of C language, following the imperative-first strategy described in CC2001 [7]. While this has been a successful approach in the near past, our experience shows that it does not lead to the development of skills to create a good C++ code. Thatâ€™s why in recent years weâ€™ve been teaching C++ to beginners in a totally different way â€“ with avoidance of C-style strings and raw arrays; delayed introduction to pointers (just after references); polymorphism with references instead of pointers; smart pointers instead of raw pointers; early use of standard library features, and writing modern C++ from day one.
EFEKTIVITAS PENGELOLAAN SEKOLAH MENENGAH KEJURUAN BERSTATUS BADAN LAYANAN UMUM DAERAH (SMK-BLUD)

It needs efforts to continue improving the quality of SMK-BLUD education from various aspects so that the operational implementation can take place optimally. The purpose of this study is to design a strategy to improve the effectiveness of SMK-BLUD through implementing school governance (SG) and total quality management (TQM) using Interpretive Structural Modeling (ISM) analysis. Data obtained through the Group Disscusion Forum (FGD) with expert experts. The results reveals that the strategic planning modeling for improving the effectiveness of SMK-BLUD management is by focusing on increasing TQM having the greatest driving power then focusing on improving of implementation of SG. This improvement can be done by improving the quality of man by fostering strong and decisive leadership in SMK-BLUDs through providing leadership training and opportunities to play roles as leader, for school principals and the teachers. Keywords: ISM, school governance, SMK-BLUD, TQM
DESIGN AND IMPLEMENTATION OF LINKAGE UPDATE MANAGEMENT SYSTEM FOR GEO-INFORMATION SERVICE PLATFORM

Abstract. The data update of the geographic information public service platform requires the joint investment of the resources of the national, provincial and municipal nodes of the platform. How to efficiently organize the collaboration of nodes at different levels is a problem that needs to be solved in the construction of the platform. The platform linkage update management system proposed in this paper is based on a workflow engine and driven by data flow. Its functions include data fusion agreement handling, data update, data access and distribution, problem feedback, announcement, statistics &amp;amp; analysis, system management and other functions.The system is suitable for the joint participation of construction and maintenance units suitable for all nodes to participate in, get through the online and offline update process and related resources.The construction of the system can better coordinate the construction of national geographic information public services, and achieve the effect of "linked update and shared services". The system provides higher efficiency for the dynamic update of the geographic information public service platform, allowing geographic information data resources to provide services for natural resource management and economic and social development in a more timely and extensive manner.
PERANGKAT AJAR PENGENALAN CHORD PADA ALAT MUSIK GITAR, KEYBOARD DAN RECORDER

AbstractAdvances in information technology allows its utilization in various fields, one of which is the field of music. The high cost of studying music in music course institutions is an obstacle for those who want to learn musical instruments and Chords. Therefore, by utilizing technological progress here the author tries to collect material about music from various sources, especially in the field of Chord recognition on guitar instruments, Keyboards and Recorders. The goal itself is that this tool can help users understand Chords, how they sound, and how to play the Chords on guitar, Keyboard and Recorder instruments to make it easy to learn music.Keywords : Music, teaching device, guitar, Keyboard, Recorder
DCNet: diffusion convolutional networks for semantic image segmentation

Semantic image segmentation makes a pixel-level classification play an essential role in scene understanding. Recently, most approaches exploit deep learning neural networks, especially convolutional neural networks (CNNs), to tackle the image segmentation challenge. Common issues of these CNN-based methods are the loss of spatial features during learning representations and the limited capacity for capturing contextual information in a large receptive field. This paper proposes a diffusion convolutional network (DCNet) to combine the CNN and graph convolutional neural network (GCNN) for semantic image segmentation. In the proposed model, diffusion convolution is formulated as a graph convolutional layer to aggregate structural and contextual information without losing spatial features. The final segmentation results on the PASCAL VOC 2012 and Cityscapes datasets show better performance than baseline approaches and can be competitive with state-of-the-art methods.
Preserving Cultural Heritage with Mobile Augmented Reality – A survey

A worldwide trend is the inclusion of multimedia in cultural heritage(CH) for preservation. This will increase the user perception as well. So the Mobile Augmented reality(MAR) technology is very much used in this respect. This paper survey the state-of-art of application of MAR in CH(MARCH). Besides, a comparative analysis of the different frameworks is done. Finally, this survey gives future research direction in this field.
Augmenting and Sampling for GAN Training and Generating Natural Adversaries

Author(s): Zhao, Zhengli | Advisor(s): Singh, Sameer | Abstract: Generative Adversarial Networks (GANs) provide a novel framework and powerful tools for machine learning, especially for deep representation learning and generative models. GANs have been successfully applied to a variety of prominent applications including image synthesis, photo editing, video prediction, text generation, and domain adaptation. Despite the development and advance of GANs, there are still crucial challenges to their applications, such as the unstable training of GANs and frequent mode collapse.In this dissertation, we develop a series of methods for improving the training of GANs from different perspectives, and utilize GANs to generate natural adversaries for evaluating the robustness and interpreting the behavior of machine learning models. We propose to apply consistency regularization to the training procedure of GANs with respect to the augmentations of both real data and generated examples, along with regularizing the generator directly with latent optimizations. We thoroughly research on the effectiveness of a broad set of image augmentations for improving generation performance when interacting with unsupervised learning techniques such as consistency regularization and contrastive loss. We also develop a simple yet effective sampling schema based on the predictions of the discriminator, which has been shown to stabilize GAN training and alleviate mode collapse. All these methods generalize to different GAN variants, result in state-of-the-art generation performance, and can be adopted as a standard part of the GAN training toolkit. Finally, as a novel application of GANs, we incorporate and utilize GANs to generate natural adversarial examples that are meaningful and semantically close to input examples for both visual and textual data, which can help evaluate and interpret black-box models.
An Intelligent Smart Ranked Feature Construction Analysis based on Clustering High Dimensional Data Streams

Artificial Intelligence is nowadays successfully applied to massive data sets assembled from various areas. One of the noteworthy challenges in applying AI methodologies to massive data sets is the way by which to effectively use accessible computational resources when building prescient and inferential models, while utilizing data in a measurably optimal manner. Random projections have been dynamically received for a differing set of tasks in AI including dimensional decrease. One explicit line of research on this point has analyzed the use of quantization rebutting in projection with the point of additional data pressure. We present a fundamental calculation, named double random projection, which uses the double solution of the low-dimensional improvement issue to recuperate the ideal solution for the first issue.. Our Hypothetical Examination (HE) shows that with a high probability, the proposed calculation can accurately recoup the ideal solution for the first issue, given that the data lattice is (roughly) low-position and ideal solution is (around) inadequate.. We further exhibit that the proposed calculation can be applied iteratively to diminishing the exponentially
The novel algorithm of dual-axis solar tracker in severe weather condition

Abstract Fulfilling the energy demand by using fossil fuel is still heavily dependent. Eco-friendly-based energy like solar energy is being employed. Solar energy possess the potential to adequately fulfil more than ten times of current energy demand, which means it is profitable in terms of energy and environment. Harvesting solar energy using solar cells has issues with its efficiency. The tracking system is believed to enhance the efficiency of solar panels. In other hand, the current system failed when it comes to severe. Global horizon irradiance in severe weather, solar radiation is scattered evenly in the horizontal plane. So, adding the diffused irradiance parameter to the algorithm hopefully tackle the double axis solar tracker (DAST) current issues. This DAST system uses Arduino Uno as the main control. Light dependent resistor (LDR) as a sensor in the DAST system. Servo motor as an actuator is used. Experiment result in sunny day, cloudy day, and rainy day, respectively 59.67Wh, 38.20 Wh, and 2.5 Wh. In other hand, a fixed tilt angle system generates 46.51 Wh on sunny day, 29.84 Wh on cloudy day, and 1.95 Wh on rainy day.
Primary and Secondary Gold Sources of Quaternary Placers of Western Spain: a Morphotextural and Compositional Analysis of the Fresnedoso Deposit.

&amp;lt;p&amp;gt;In this contribution we have investigated the Fresnedoso Quaternary gold placer (Western Spain), analyzing the morphotextural and microchemical evolution of gold particles. The statistical analysis has revealed the presence of two populations of particles being consistent with primary sources situated at a distal [20 - 50 km] and a proximal [2.5 - 10 km] range. The gold morphology and chemistry point to a recycling (and potentially undiscovered) Tertiary paleoplacers. The discovery of primary laminar morphologies points to lode deposits in small-flat veins hosted in Precambrian metasediments (Schist Greywacke Complex). All these findings suggest that the Fresnedoso gold deposit is formed by mono and polycyclic particles. We have tested previous transport distance vs Flattening indexes (CFI, Shilo) models resulting in useful framework for exploration of undiscovered ores, even with a small sample dimension. Chemical analysis of the different gold morphologies depicted that the Fresnedoso gold is a AuAg bimetallic alloy. Three groups were identified based on the texture and composition of the gold particles: Type 1 (Au1= Au89-94Ag11-6), Type 2 (Au2= Au99 Ag1) and Type 3 (Au3~ Au &amp;gt;99). Particle's cores (gold Type 1) show a compositional range that could be interpreted as differences in primary sources, spatial dispersion of sources or the actuation of secondary processes, probably in an orogenic gold context. Microchemical heterogeneity in the particles is probably due to secondary processes. A conceptual model has been elaborated to explain particle's microchemical domains represented by gold Type 2 (rim) and Type 3 (micro-aggregates) as the result of two different de-alloying stages: A) initial Ag-leaching at the rim and/or through microcracks and grainboundaries (Type 2), B) Total reset of the primary chemical fingerprint, with porous microtexture and the precipitation of gold with iron oxyhydroxides and clays (Type 3). This model suggests a silver de-alloying mechanism favored in a chlorine-iron-rich environment as in the case of laterites. Deformation and eventually recrystallization mechanisms associated with the fluvial transport (mechanical cold-work), cooperated in the evolution of the particles (Dos Santos et al. 2020).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;References&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Dos Santos Alves, K., Barrios S&amp;amp;#225;nchez, S., G&amp;amp;#243;mez Barreiro, J., Merinero Palomares,R. and Compa&amp;amp;#241;a Prieto, J.M. (2020). &amp;quot;Morphological and compositional analysis of alluvial gold: The Fresnedoso gold placer (Spain).&amp;quot;&amp;amp;#160;Ore Geology Reviews&amp;amp;#160;: 103489.&amp;lt;/p&amp;gt;
A Semantics for “Typically” in First-Order Default Reasoning

We present a new semantics for first-order conditional logic, which is a generalization of that of Friedman, Halpern and Koller [7]. We utilize Fitting’s embedding of first-order classical logic in first-order S4 to define our semantics. We explain our semantics by showing how it works on the connective expressing “typically implies”. We argue that it has a number of good properties, in particular, it is more adjustable to special situations than that of [7]. For example, we can make sense of nested conditional implications even when a conditional implication does not necessarily hold on the entire set of possible worlds, but where-ever it is satisfied, the conclusion is typically satisfied.
Does “See One and Do One” Work in Histology?: A Two‐Phase Cross‐Over Eye Tracking Study on Complex Histological Pattern Recognition Skills Acquisition

Histology, the study of tissues, is a visual subject that requires competency in pattern recognition. Histology education has evolved over the past several decades, driven by both the advent of technology like virtual microscopy and curricular changes in medical education which have significantly reduced classroom contact hours. As a result, much histology education now occurs in integrated curricula and uses a virtual platform to teach pattern recognition. Although the many advantages of virtual microscopy have been well documented, few studies have investigated how students acquire pattern recognition skills in histology. In a previous eye‐tracking study using simple blood smear visual fields, feedforward training – seeing how an expert scans a visual field to identify leukocyte types – seemed to help students gain visual literacy more efficiently, as evidenced by increased average fixation duration (eyes lingering on visually salient areas) and decreased scan path distance (less eye roaming). Expanding on this work, the goal of the current study was to investigate the educational efficacy of feedforward training in histology with much more complex systemic tissue images, containing significantly more visually salient regions and distractors. In a two‐phase cross‐over design, first year graduate students in a histology course were randomized into two groups: Group A and Group B. In the first phase, Group A was exposed to feedforward training with gastrointestinal tract tissue images over which an expert's eye scan paths were displayed, while Group B, as a control group, viewed the same series of tissue images but without the expert's scan paths. In the second phase, Group B was exposed to feedforward training with lymphatic system tissue images with the expert eye scan paths, while Group A served as the control group. In both phases, all other visual content, exposure time, and data collection tools were identical between the two groups. Participants were tasked to identify various organs or regions from series of relevant tissue images. During these tasks, participants' eye movements were recorded and analyzed to calculate identification accuracy and speed, and fixation number and duration. A total of 1848 data sets were collected following COMIRB No. 17‐1324 protocol and analyzed using MATLAB. Preliminary results reveal no significant differences in identification accuracy (t= −1.0914, p= 0.28808), identification speed (t= 0.18468, p= 0.85534), fixation number (t= 0.49266, p= 0.62762), or fixation duration (t=0.36483, p= 0.71906) between the experimental and control groups. The current study suggests that with more complex histological visual fields, the educational impact of feedforward training may be attenuated, and suggests a revised feedforward mechanism or alternate educational modalities for histology education should be explored. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
ОҚУШЫЛАРҒА РОБОТОТЕХНИКА НЕГІЗДЕРІН ОҚЫТУДЫҢ ӘДІСТЕМЕЛІК ЕРЕКШЕЛІКТЕРІ

The main tasks of modern education are: training, education and development. In their professional activities, the teacher guarantees the continuous implementation of these processes, their harmonious combination and strives to ensure the effectiveness of the educational process. The approach to competence in General secondary education objectively corresponds to social expectations in the field of education and the interests of participants in the educational process. The competence approach is an approach that takes into account learning outcomes, so the result of learning is not a set of information received, but the ability of students to respond to various problem situations. Therefore, in the secondary education system, a set of tasks on the basics of robotics is used as a tool for teachers to achieve educational goals
Code Clone Detection using Code2Vec

Author(s): Prasad, Anupriya | Advisor(s): Lopes, Cristina V. | Abstract: Code Clone detection is important in software engineering as it aims at solving various problems like code maintenance, identification, code reuse, scalability, and plagiarism. Software development revolves around implementing logic using tools and technologies where every developer has a different coding style and logical approach to reach required goals. However, the end result of many implementations can be the same. This is where the need for code maintainability, reusability, and optimization arises. Code clone detection can help to leverage the immensely large source codes available on the web to attenuate code writing time by reusing sources available online. Clone detection in source code is based on the similarity of the program content or similarity in the program functionality. There are many techniques that have been tried and tested in the literature. However, these naive approaches do not perform adequately for higher-level clones. In this thesis, I am exploring deep learning based technique Code2Vec. In order to identify, compare, and reuse an existing piece of code, deep learning techniques can help to predict if a similar implementation source code exists in a codebase or a dataset of codes. In this thesis, the approach of representing code in the form of vectors and applying Natural Language Processing for code clone detection has been discussed. The scope of the thesis is to devise an approach for the detection of similar functional methods in Java GitHub code repositories, expanding on the Code2Vec model [1]. I demonstrate the capabilities of applying the Code2Vec model to Java source code in order to determine the path vectors for method similarity detection. Furthermore, I discuss the design, architecture, and usage of the model. An in-depth analysis of preprocessing mechanisms, data collection, data preprocessing is also highlighted. In this thesis, I apply a normalization technique to update the variable names in the Code2Vec approach and compare the baseline Code2Vec model with the normalized model. The comparison shows improved precision results in the normalized code clone detection approach. Finally, the benefits of my approach and a detailed analysis of results on a dataset with Java methods are presented. The results are evaluated on the basis of Recall and Precision. I evaluate the recall with the help of BigCloneBench and precision using InspectorClone and MeasurePrecision open-source tools.
Practical Model Intelligence Computer Assisted Instruction project-based learning-oriented High Order Thinking Skill

In order to adapt to the challenges of the 21st century, efforts to improve the quality of education must be made through the provision of communication skills, teamwork skills, critical thinking, and problem-solving skills as well as innovative orientation and creativity for students. In this connection, of course, learning in the 21st century has made ICT an important part of the learning process through the growth of constructivism and constructivism. For this reason, this study was conducted to measure the practicality of the Intelligence Computer Assisted Instruction model of project-based learning-oriented at Higher Level Thinking Skills for lecturers and students, so that practical models can be used in learning. This study uses the Borg and Gall model steps by assessing the practicality of a model that has previously been declared valid. The subject of practicality testing is a lecturer who teaches an expert system course, a trial for students who take an expert system course. The instrument used was a practicality questionnaire. The data analysis technique used the Akien test to see the practicality of the model being developed. The results of the project-based learning model of Intelligence Computer Assisted Instruction practicum oriented at Higher Level Thinking Skills for lecturers and students which contain model books, lecturer manuals, and teaching modules are classified as very practical. From these results, it is stated that the project-based Intelligence Computer Assisted Instruction learning model that is oriented towards High-Level Thinking Skills is stated to have a practicality that is feasible to use