Reviewerly/exHarmony huggingface dataset:
PERFORMANCE VALIDATION OF PRIOR QUANTIZATION TECHNIQUES IN OUTLIERS CLASSIFICATION USING WDBC DATASET

Data mining is the process of analyzing enormous data and summarizing it into the useful knowledge discovery and the task of data mining approaches is growing quickly, particularly classification techniques very efficient, way to classifying the data, which is important in the decision-making process for medical practitioners. This study presents the quantization and validation (OQV) techniques for fast outlier detection in large size WDBC data sets. The distance metrics utilization makes the algorithm as the linear one for various objects and assures the sequential scanning. The inclusion of direct quantization technique and the cluster explicit discovery assures the simplicity and the economical. The comparative analysis of proposed OQV techniques with the triangular boundary-based classification and the Weighing-based Feature Selection and Monotonic Classification (WFSMC) regarding the accuracy, precision, recall and the number of attributes assures an effectiveness of OQV for large size datasets.
UNDERSTANDING WEB TRAFFIC ACTIVITIES USING WEB MINING TECHNIQUES

Web Usage Mining is a computational process of discovering patterns in large data sets involving methods using the artificial intelligence, machine learning, statistical analysis and database systems with the goal to extract valuable information from accessing server logs of World Wide Web data repositories and transform it into an understandable structure for further understanding and use. Main focus of this paper will be centered on exploring methods that expedites the log mining process and present the result of log mining process through data visualization and compare data-mining algorithms. For the comparison between classification techniques, precision, recall and ROC area are the correct measures that are used to compare algorithms. Based on this study it shows that Naïve Bayes and Bayes Network are proven to be the best algorithms for that.
TECHNICAL LITERACY OF ELEMENTARY SCHOOL PUPILS, STATUS AND PERSONALITY OF TECHNICAL SUBJECTS TEACHER

The current company is characterized by the use of modern technologies, informatics and upcoming robotics and automation.As a result, technology has spread to all areas of human life and work.It is desirable for the pupil to have technical education, to use the acquired knowledge in the field of machine operation, material processing, physical and chemical properties of individual types of material.Modern technical time requires active computer work.Teaching the subject of technology at elementary school is provided by a knowledgeable teacher with technical education.
Subgraph and object context‐masked network for scene graph generation

Scene graph generation is to recognise objects and their semantic relationships in an image and can help computers understand visual scene. To improve relationship prediction, geometry information is essential and usually incorporated into relationship features. Existing methods use coordinates of objects to encode their spatial layout. However, in this way, they neglect the context of objects. In this study, to take full use of spatial knowledge efficiently, the authors propose a novel subgraph and object context‐masked network (SOCNet) consisting of spatial mask relation inference (SMRI) and hierarchical message passing (HMP) modules to address the scene graph generation task. In particular, to take advantage of spatial knowledge, SMRI masks partial context of object features depending on their spatial layout of objects and corresponding subgraph to facilitate their relationship recognition. To refine the features of objects and subgraphs, they also propose HMP that passes highly correlated messages from both microcosmic and macroscopic aspects through a triple‐path structure including subgraph–subgraph, object–object, and subgraph–object paths. Finally, statistical co‐occurrence probability is used to regularise relationship prediction. SOCNet integrates HMP and SMRI into a unified network, and comprehensive experiments on visual relationship detection and visual genome datasets indicate that SOCNet outperforms several state‐of‐the‐art methods on two common tasks.
THE ROLE OF BUSINESS MODELS IN ORGANIZATIONAL REPUTATION CAPITAL: ANALYTICAL RESEARCH IN A SAMPLE OF PRIVATE COLLEGES IN BAGHDAD CITY

Given the intensity of competition in the private higher education sector and the large number of private colleges and universities and the desire to upgrade the level of this sector, the researcher chose to apply this research.What is the nature of the relationship between these two variables?The importance of the research lies in forming clear visions for the society and the research sample about the main variables and its constituent dimensions.For the reputational capital of the organization, the research in the private higher education sector was applied to Al-Turath and Al-Rafidain faculties where the final research sample reached a total of ( 71) individuals.The researcher on a set of statistical tests such as (validation factor validation, test Alpha Kronbj, averages, standard deviation ...), the faculties of heritage and Mesopotamia University interested in business models and capital of organizational hearing and the dimensions of each, continue and increase support for all activities that Of his business Upgrading the concept of business models and the concept of a reputable organizational capital.
What Happens after the Primal Burn? Dissonance in Sonic Youth’s Middle Period

Sonic Youth originated in No Wave, a movement from the late 1970s and early 1980s that reduced rock to minimal gestures and explored extremes of noise. In the mid-1980s, Sonic Youth’s style changed as they began to incorporate guitar parts that were reminiscent of 1970s hard rock. But their experimental tendencies persisted through this change, because they overlaid the parts in ways that created incongruity and tweaked hard-rock stylistic features in order to create dissonance or tonal conflict. Sonic Youth’s strategies for twisting hard-rock norms into clashing harmonies often follow one of two recurring types. The first, tonic divergence , occurs when separate lines have phase-mismatched tonic harmonies. The second, intervallic dissonance , occurs when instrumental lines are arranged in order to highlight harshly dissonant intervals or chromatic clusters. In many songs, their dissonant counterpoint works in tandem with their characteristic noisy guitar timbres by occurring in alternation, forcing listeners to continually re-evaluate how they perceive a song as a standard rock track. The analyses show how the band continued to experiment within popular style and created types of dissonance that influenced 1980s–1990s guitar-based indie rock.
Intermediate Representation Using Graph Visualization Software

In this paper, a method to initiate, develop and visualize an abstract syntax tree (AST) in C++ source code is presented.The approach is in chronological order starting with collection of program codes as a string and split into individual characters using regular expression.This will be followed by separating the token grammar using best first search (BFS) algorithm to determine node having lowest value, lastly followed by graph presentation of intermediate representation achieved with the help of graph visualization software (Graph-Viz) while former is implemented using python programming language version 3. The efficacy of our approach is used in analyzing C++ code and yielded a satisfactory result.
Sistem Monitoring Kolesterol Melalui Iris Mata dengan Metode Pengolahan Citra

Early detection to determine the presence or absence of cholesterol in the body is a necessity for everyone who wants to live healthy. Many diseases can be caused by the presence of cholesterol such as heart disease, stroke, nerve disorders, kidney problems, hypertension, and etc. Therefore, cholesterol detection must be done regularly. This study discusses about the cholesterol detection system through the iris eyes using image processing and monitoring progress in continously. Detection of cholesterol can be observed with Arcus Senilis or a gray ring in the iris eyes. Tests carried using 15 samples which cholesterol identifed. The process of image processing consists of image acquisition, sharpening, segmentation, convert grayscale and binary images. Cholesterol can be identify with difference between pixel values 0 (black) and pixel values 1 (white) in binary images. Research data will be stored in an Excel format database with adding some user data. From the test, results analysis carried the try and error threshold method using values of 80, 100, 150, and getting an accuracy of 87%, 73%, and 33%. Besides, monitoring cholesterol can be carried using a system interface and database with adding the required data and can display it on excel.
Anomaly Detection of Folding Operations for Origami Instruction with Single Camera

When people learn a handicraft with instructional contents such as books, videos, and web pages, many of them often give up halfway because the contents do not always assure how to make it. This study aims to provide origami learners, especially beginners, with feedbacks on their folding operations. An approach for recognizing the state of the learner by using a single top-view camera, and pointing out the mistakes made during the origami folding operation is proposed. First, an instruction model that stores easy-to-follow folding operations is defined. Second, a method for recognizing the state of the learner's origami paper sheet is proposed. Third, a method for detecting mistakes made by the learner by means of anomaly detection using a one-class support vector machine (one-class SVM) classifier (using the folding progress and the difference between the learner's origami shape and the correct shape) is proposed. Because noises exist in the camera images due to shadows and occlusions caused by the learner's hands, the shapes of the origami sheet are not always extracted accurately. To train the one-class SVM classifier with high accuracy, a data cleansing method that automatically sifts out video frames with noises is proposed. Moreover, using the statistics of features extracted from the frames in a sliding window makes it possible to reduce the influence by the noises. The proposed method was experimentally demonstrated to be sufficiently accurate and robust against noises, and its false alarm rate (false positive rate) can be reduced to zero. Requiring only a single camera and common origami paper, the proposed method makes it possible to monitor mistakes made by origami learners and support their self-learning.
Not Distributive Lattice Over Field of Residue Classes Modulo 5

The symmetry in algebra usually allows a undirected path between the source and sink in any discussion.So, creating a super algebra in the form of a lattice over a known algebra which does not admit associativity or commutativity or distributivity will help a directed path which has no retreat.Keeping this view point, i have created 3 not distributive lattices over different known fields which can be used in the cryptography techniques of varied approach.This is the 4 th lattice over somewhat bigger field where the lattice is constructed using the suitable definitions of disjunction and conjunction that allows the failure of distributive property and the lattice is a field extension over the residue classes modulo 5.
Seismic Activity Investigation of Elazıg and Surroundings, Eastern Anatolia

This abstract book contains abstracts of the various research ideas presented at The Second Eurasian RISK-2020 Conference and Symposium.The RISK-2020 Conference and Symposium served as a perfect venue for practitioners, engineers, researchers, scientists, managers and decision-makers from all over the world to exchange ideas and technology about the latest innovation developments dealing with risk minimization.
Use of Scanning as a Complementary Method for Forensic Document Expertise

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Semantic Transformation of Precedent Names in Japanese Songs

The paper attempts to identify semantic transformations of precedent names in Japanese songs. Material of practical research is based on the texts of 62 songs modern Japanese electronic band 水曜日のカンパネラ (Suiyoubi no Kanpanera). The choice of this artist is due to the current popularity of the groups among the number of new wave Japanese artists of the 2010s. The authors offer their thematic classification of precedent names in the song’s lyrics. In the course of the research, the authors come to the conclusion that not all precedent names remain in the original, primordial meaning, but can have either several meanings or radically change the meaning. The authors see the main reason for semantic transformations in the dynamically changing everyday life of the Japanese and their communication, as well as in the admirers of these songs, which are usually young people and adolescents.
SALIENT AREA DISCERNMENT VIA HIGH DIMENSIONAL COLOR TRANSFORM AND LOCAL SPECIAL PLATFORM

Detecting visually salient regions in images is fundamental problems and it is useful for applications like image segmentation, adaptive compression, and object recognition. A salient object region is a soft decomposition of foreground and background image elements. To detect salient regions in an image in terms of the saliency maps. To create a saliency map by using a linear combination of colors in high-dimensional color space. To improve the performance of saliency estimation, utilize the relative location and color contrast between superpixels. To resolve the saliency estimation from trimap by using learning based algorithm. This is based on an examination that salient regions frequently have individual colors’ compared with backgrounds in human sensitivity however, human perception is complicated and extremely nonlinear. The tentative outcome on three benchmark datasets show that our approach is valuable in assessment with the prior state-of-the-art saliency estimation methods. Finally, salient region detection that outputs full resolution saliency map with well-defined boundaries of the salient object.
CLUENER2020: Fine-grained Name Entity Recognition for Chinese

In this paper, we introduce the NER dataset from CLUE organization (CLUENER2020), a well-defined fine-grained dataset for named entity recognition in Chinese. CLUENER2020 contains 10 categories. Apart from common labels like person, organization, and location, it contains more diverse categories. It is more challenging than current other Chinese NER datasets and could better reflect real-world applications. For comparison, we implement several state-of-the-art baselines as sequence labeling tasks and report human performance, as well as its analysis. To facilitate future work on fine-grained NER for Chinese, we release our dataset, baselines, and leader-board.
Learning Discriminative Neural Sentiment Units for Semi-supervised Target-Level Sentiment Classification

Target-level sentiment classification aims at assigning sentiment polarities to opinion targets in a sentence, for which it is significantly more challenging to obtain large-scale labeled data than sentence/document-level sentiment classification due to the intricate contexts and relations of the target words. To address this challenge, we propose a novel semi-supervised approach to learn sentiment-aware representations from easily accessible unlabeled data specifically for the fine-grained sentiment learning. This is very different from current popular semi-supervised solutions that use the unlabeled data via pretraining to generate generic representations for various types of downstream tasks. Particularly, we show for the first time that we can learn and detect some highly sentiment-discriminative neural units from the unsupervised pretrained model, termed neural sentiment units. Due to the discriminability, these sentiment units can be leveraged by downstream LSTM-based classifiers to generate sentiment-aware and context-dependent word representations to substantially improve their sentiment classification performance. Extensive empirical results on two benchmark datasets show that our approach (i) substantially outperforms state-of-the-art sentiment classifiers and (ii) achieves significantly better data efficiency.
AN OPTIMAL FORECASTING OF LATENT SIMILARITY BASED RELATIONSHIP ON USER ATTRIBUTES USING LINK PREDICTION

Communal is one of the common words. A billion of peoples share or have certain attitudes and interests in common. By sharing and receiving the information such as text, image, audio, video etc., this kind of information, it improves the global knowledge, easily distinct the good and bad things. Now days, Social media is a good platform to share the content collectively with collaboration. Digital technologies are spread all over the global rapidly. It is an efficient way to improve the knowledge via Communal. People do not show the attention to join the community because of addiction, hacks the personal data and get misused. So that people have lack of awareness to join and use the communities. To overcome the above reasons and also all the peoples have to access and gain the information without any dilemmas. The proposed system provides the platform to link the peoples via Communal much more and gather the information all over the world with secure authentication. Anywhere in the world, every person can share and learn their thoughts with everyone. It consist of two phase to implement the proposed system. The first phase is to identify the neighbourhood and link the data. Here use Interest based FGM algorithm to predict the neighbour and link within the environment. So that each person will know all the information. Second phase, decision process to detect the person who are all link with particular communities across globally with the help of decision tree. People from anywhere to access all the data with anyone. It is easy way to equip people in all kind of innovative ideas as soon as possible.
Assessing Car Damage using Mask R CNN

Picture based vehicle protection handling is a significant region with enormous degree for mechanization. In this paper we consider the issue of vehicle harm characterization, where a portion of the classifications can be fine-granular. We investigate profound learning based procedures for this reason. At first, we attempt legitimately preparing a CNN. In any case, because of little arrangement of marked information, it doesn't function admirably. At that point, we investigate the impact of space explicit pre-preparing followed by tweaking. At last, we explore different avenues regarding move learning and outfit learning. Trial results show that move learning works superior to space explicit tweaking. We accomplish precision of 89.5% with blend of move and gathering learning.
Noise Removal from Brain MRI Images Using Adaptive Bayesian Shrinkage

In MR machine, surface coils, especially phased-arrays are used extensively for acquiring MR images with high spatial resolution. The signal intensities on images acquired using these coils have a non-uniform map due to coil sensitivity profile. Although these smooth intensity variations have little impact on visual diagnosis, they become critical issues when quantitative information is needed from the images. Sometimes, medical images are captured by low signal to noise ratio (SNR). The low SNR makes it difficult to detect anatomical structures because tissue characterization fails on those images. Hence, denoising are essential processes before further processing or analysis will be conducted. They found that the noise in MR image is of Rician distribution. Hence, general filters cannot be used to remove these types of noises. The linear spatial filtering technique blurs the object boundaries and degrades the sharp details. The existing works proved that Wavelet based works eliminates the noise coefficient that called wavelet thresholding. Wavelet thresholding estimates the noise level from high frequency content and estimates the threshold value by comparing the estimated noisy wavelet coefficient with other wavelet coefficients and eliminate the noisy pixel intensity value. Bayesian Shrinkage rule is one of the widely used methods. It uses for Gaussian type of noise, the proposed method introduced some adaptive technique in Bayesian Shrinkage method to remove Rician type of noises from MRI images. The results were verified using quantitative parameters such as Peak Signal to Noise Ratio (PSNR). The proposed Adaptive Bayesian Shrinkage Method (ABSM) outperformed existing methods.
An ENAS Based Approach for Constructing Deep Learning Models for Breast Cancer Recognition from Ultrasound Images.

Deep Convolutional Neural Networks (CNN) provides an end-to-end solution for image pattern recognition with impressive performance in many areas of application including medical imaging. Most CNN models of high performance use hand-crafted network architectures that require expertise in CNNs to utilise their potentials. In this paper, we applied the Efficient Neural Architecture Search (ENAS) method to find optimal CNN architectures for classifying breast lesions from ultrasound (US) images. Our empirical study with a dataset of 524 US images shows that the optimal models generated by using ENAS achieve an average accuracy of 89.3%, surpassing other hand-crafted alternatives. Furthermore, the models are simpler in complexity and more efficient. Our study demonstrates that the ENAS approach to CNN model design is a promising direction for classifying ultrasound images of breast lesions.
Efficient lambda encodings for Mendler-style coinductive types in Cedille

In the calculus of dependent lambda eliminations (CDLE), it is possible to define inductive datatypes via lambda encodings that feature constant-time destructors and a course-of-values induction scheme. This paper begins to address the missing derivations for the dual, coinductive types. Our derivation utilizes new methods within CDLE, as there are seemingly fundamental difficulties in adapting previous known approaches for deriving inductive types. The lambda encodings we present implementing coinductive types feature constant-time constructors and a course-of-values corecursion scheme. Coinductive type families are also supported, enabling proofs for many standard coinductive properties such as stream bisimulation. All work is mechanically verified by the Cedille tool, an implementation of CDLE.
Influence Paths for Characterizing Subject-Verb Number Agreement in LSTM Language Models

LSTM-based recurrent neural networks are the state-of-the-art for many natural language processing (NLP) tasks. Despite their performance, it is unclear whether, or how, LSTMs learn structural features of natural languages such as subject-verb number agreement in English. Lacking this understanding, the generality of LSTM performance on this task and their suitability for related tasks remains uncertain. Further, errors cannot be properly attributed to a lack of structural capability, training data omissions, or other exceptional faults. We introduce *influence paths*, a causal account of structural properties as carried by paths across gates and neurons of a recurrent neural network. The approach refines the notion of influence (the subject's grammatical number has influence on the grammatical number of the subsequent verb) into a set of gate or neuron-level paths. The set localizes and segments the concept (e.g., subject-verb agreement), its constituent elements (e.g., the subject), and related or interfering elements (e.g., attractors). We exemplify the methodology on a widely-studied multi-layer LSTM language model, demonstrating its accounting for subject-verb number agreement. The results offer both a finer and a more complete view of an LSTM's handling of this structural aspect of the English language than prior results based on diagnostic classifiers and ablation.