Reviewerly/exHarmony huggingface dataset:
TWITTER SENTIMENT ANALYSIS FOR PRODUCT REVIEWS TO GATHER INFORMATION USING MACHINE LEARNING TECHNIQUE

The concept of sentiment analysis of twitter data and semantic analysis with the augmentation of machine learning methodologies has become a hot topic in recent years. Many strategies have been presented in the area of sentiment analysis in the last few years to evaluate social media data and produce a graphical presentation towards a certain business. Sentiment analysis shows you how people feel about a product or brand when penning a social media message about it. This is crucial information if you know that one persons opinion of a firm or its products might impact the opinions of others. Like many other online data mining systems, sentiment analysis platforms are based on Support Vector Machine algorithms. This algorithm, in this situation, recognises specific terms as positive or negative, indicating whether or not your brand is being adored or floored. So, in this paper, we first pre-processed the dataset, then extracted the adjectives from the dataset that have some meaning (feature vector), then selected the feature vector list, and finally applied machine learning based classification algorithms such as Nave Bays, Maximum entropy, and SVM, as well as the Semantic Orientation based WordNet to extract synonyms and similarity for the content feature. Finally, we evaluated the classifiers performance in terms of recall, precision, and accuracy.
Assessment of Waste Management through Mobile Edge Computing and Deep Learning

Due to the random occurrences of street waste, city managers usually spend a lot of energy and money cleaning street garbage, which is a core duty in computer vision, with applications ranging across the process of smart city creation. Deep network solutions are frequently constrained by the amount of training data available as they become deeper and more complicated. With this in mind, Open CV or Google AI has made the Open Images dataset publicly available in order to drive breakthroughs in image analysis and interpretation. Open Images continues the legacy of PASCAL VOC, Image Net, and COCO, but on a much larger scale. As a result, visual street cleanliness assessment will be extremely vital in this project. Existing assessment methods, on the other hand, have several significant drawbacks, such as the lack of automation in the collecting of street waste data and the lack of real-time street cleanliness data. Finally, the findings are fed into a framework for calculating street cleanliness, which allows for the visualisation of street cleanliness. Cleanliness levels are maintained at a high level, making it easier for city managers to schedule clean-up crews.
Design and Implementation of Automobile Sales Management Information System Based on C#.NET Technology

Sales determine the survival and development space of an enterprise, and the vitality of an enterprise is more directly reflected in the sales performance of the enterprise, which is of significant significance to the improvement of an enterprise's core competitiveness. This thesis is based on the design and implementation of a sales management system based on C#.NET built by an automobile sales service company as an enterprise prototype. It can achieve effective management of sales information, improve sales efficiency, and reduce sales costs; organically integrate sales information with logistics information and other departmental information to improve the company's information management level; combine the .NET framework and B/S structure to design multiple modules Sales management system efficiency increased by 6.5%
Caricaturing the prophet: Pushing the right to free speech too far?

Despite growing evidence that production of cartoons and caricatures of Muhammad causes deep hurt to Muslims across the world, European leaders are refusing to restrict their publication in the name of free speech. This article questions this position on three counts: one, the hurt these cartoons cause to the Muslims and the resulting frictions between Europe and leaders of the Muslim countries; two, the harm they cause to European societies by increasing the tension between Muslims and ordinary citizens and the state at a time when Islamophobia and anti-immigrant feelings are already high; and, three, the need to question the intent of those publishing the cartoons given the overwhelming evidence on the first two counts. The article also questions the legitimacy and usefulness of labelling attacks triggered in response to these publications as ‘Islamic terrorism’ when the profiles of those perpetuating these attacks show criminal and deviant behaviour than evidence of religious devotion. The usefulness of using labels that make actions of a few taint whole of Islam, and Muslims as a collective, needs to be debated.
PBL Education Support System to Visualize Students' Attitudes

We have built a system that supports PBL education called AI teachers. In this paper, we will introduce the mechanism and effect of group activities supported by AI teachers. In particular, we will explain the advice function for students using an emotion analysis. The problem of group activity was that when the students were divided into multiple groups and did group work in breakout rooms, the teacher could not grasp the situation of the students. Teachers can check by entering the breakout room of each group in turn, but teachers cannot see the whole at once. AI teachers have the ability to leave the voice of group activities as text. By utilizing this function, teachers can now give appropriate advice to each group. However, it takes some time to understand the student situation from the texts. Therefore, we added the emotion analysis to this function to improve the understanding the situation so that teachers can visually grasp the student&#x2019;s situation. In this paper we reports the details of this function and the results of its use.
ANALYSIS OF SURFACE WATER QUALITY BY PHYSICO-CHEMICAL PARAMETERS IN RICE (ORYZA SATIVAL.) FIELDS IN ONATTUKARA WETLANDS REGIONS, KERALA

This report presents a study of surface water physico - chemical parameters in rice fields in the Onattukara wetland zone. The research was carried out for a year, from July 2017 to June 2018. Water is highly polluted with different contaminants. Human existence depends on the availability of water. Water samples were taken monthly from surface water and examined for Temperature(Ã‚Â°C), pH, Electrical conductivity, Turbidity(JTU), Chloride(mg/l), Fluoride(mg/l), Sodium(mg/l), Potassium(mg/l), Nitrate(mg/l), Total alkalinity(mg/l), Magnesium(mg/l), Iron(mg/l), Calcium(mg/l), Sulphate(mg/l), DO (Dissolved oxygen) (mg/l), BOD (Biological oxygen Demand) (mg/l). The goal of this research was to determine the present state of Physico-chemical properties.
Development and Effect analysis of Science class using Digital devices: Focusing on Core Competencies, Science-related Attitude and Science Achievement

Seo Yejeong);김현경(Kim Hyunkyung)
Building a 3D Form to Recognize Facial Images

There are several problems with the ability to recognize facial images, so the image was built in a 3D format, which allows users to evaluate and form the necessary properties of the entire object, while it is impossible to do this in two-dimensional form. Reprinting an object in an image form enables most of the properties of the target image by using layer-based neural networks that learn sequentially with discrete mathematical structures. A two-dimensional image is taken, and then the 3D shape is used in the image to spatially renovate it while different noise levels and different lighting levels are considered. This paper aims to show that the model for reconstructing the three-dimensional image reveals the dilemma of defining the basic characteristics of the image as a whole in all cases of interference, even if the viewpoint of taking the image changes. The processing has several stages. First, input the data processing result obtained in the previous level into the next level input to get the final result. After training, the first level sequences are represented as graphs, and then the input image data is sent to the first layer of the recognition model to calculate h. Consistent activation of learning validity for each subsequent level of the proposed model of reprinting an object in an image completely solves the problem of identifying a person from the front image as a whole under interference and regardless of the change in the perspective image.
Retracted: Visual Saliency Analysis of Artistic Photography based on Single-Angle Image Reconstruction Algorithm with Attention Model

Aiming at the limitations of existing visual saliency models that do not have universal applicability, this paper analyzes the visual saliency Is model based on single-angle image reconstruction. In order to solve the problem that the saliency of the IS model is not ideal when describing large targets, this model is used the method is simple, the amount of calculation is small, and the feasibility is good. The similar area is assigned a similar saliency value as a constraint. The engineering algorithm needs to downsample the original image. The saliency map is only the original image. The algorithm has high computational efficiency and the output saliency map It is the full-resolution algorithm that avoids image segmentation, so the effect is very good for images that are difficult to automatically segment.
The Dual-Natured Direction of Intellectual Capital Formation in the System of Higher Education

Introduction. In а modern world, higher education institutions (HEIs) play a key role in the development of science, technology, industry, economics and, most importantly, the development of humans, their individual and social consciousness, which are key innovative resources for sustainable development. At the same time, the specificity of the HEIs’ intellectual capital leads to the formulation of a paradox: higher education is both the most and the least (at least domestic one) intellectualized economic structure. HEIs are a source of knowledge, their staff, students and graduates – its carriers and, at the same time, domestic universities are stagnating in their development, not using this knowledge as a value-generating income factor.&#x0D; Aim and tasks. The purpose of the study is to develop a model in order to analyze the formation and implementation of HEIs’ intellectual capital in the entire structure of their economic activities. The model is proposed in order to identify the motives and ways of its development and the awareness of its main feature – a dual-natured direction, manifested for universities, scientific and teaching staff, students, business and state.&#x0D; Results. The article deals with an analysis of possible directions for achieving the goals of the HEI in the implementation of its chosen business model, as well as the relationship of elements of intellectual capital, should be based on the proposed model, which allows its management to ensure the synergistic effect of the totality of its components.&#x0D; Conclusions. The study presents the main sources of innovative advantage, and also describes how the intellectual capital of the HEI is related to its market value. It is a process of commercialization of knowledge, which generates intellectual capital at the HEI, giving it not only scientific and educational value, but also market value. Collaboration between HEIs and industry offers researchers the opportunity to increase the value of their intellectual capital through monetary or contextual support for research, which contributes to their greater scientific productivity. As for students, this allows them, in particular, to gain practice, learn to apply theoretical knowledge in practice and respond to business needs.
Gender Recognition System Using Convolutional Neural Network

Human gender detection which is a part of facial recognition has received extensive attention because of it’s different kinds of application. Previous research works on gender detection have been accomplished based on different static body feature for example face, eyebrow, hand-shape, body-shape, finger nail etc. In this research work, we have presented human gender classification using Convolution Neural Network (CNN) from human face images as CNN has been recognised as best algorithm in the field of image classification. To implement our system, at first a pre-processing technique has been applied on each image using image processing. The pre-processed image is passed through the Convolution, RELU and Pooling layer for feature extraction. A fully connected layer and a classifier is applied in the classification part of the image. To obtain a better result, we have implemented our system using different optimizers. We use libraries like , Keras , Opencv and also uses Tensorflow as backend.
MARF: Multiscale Adaptive-Switch Random Forest for Leg Detection With 2-D Laser Scanners

For the 2-D laser-based tasks, e.g., people detection and people tracking, leg detection is usually the first step. Thus, it carries great weight in determining the performance of people detection and people tracking. However, many leg detectors ignore the inevitable noise and the multiscale characteristics of the laser scan, which makes them sensitive to the unreliable features of point cloud and further degrades the performance of the leg detector. In this article, we propose a multiscale adaptive-switch random forest (MARF) to overcome these two challenges. First, the adaptive-switch decision tree is designed to use noise-sensitive features to conduct weighted classification and noise-invariant features to conduct binary classification, which makes our detector perform more robust to noise. Second, considering the multiscale property that the sparsity of the 2-D point cloud is proportional to the length of laser beams, we design a multiscale random forest structure to detect legs at different distances. Moreover, the proposed approach allows us to discover a sparser human leg from point clouds than others. Consequently, our method shows an improved performance compared to other state-of-the-art leg detectors on the challenging Moving Legs dataset and retains the entire pipeline at a speed of 60+ FPS on low-computational laptops. Moreover, we further apply the proposed MARF to the people detection and tracking system, achieving a considerable gain in all metrics.
RELATIONSHIP BETWEEN INTELLECTUAL ABILITIES AND DIGITAL COMPETENCE OF ADOLESCENTS

The paper notes that for the successful digital socialization of adolescents, it is necessary to develop intellectual abilities and form critical thinking. The purpose of the research is to study the relationship between digital competence and intellectual abilities of adolescents. As a methodological tool, we used: the "digital competence index" method, the "progressive matrices" test by D. Raven, the TSI subtests of "the structure of intelligence test" by R. Amthauer, the critical thinking test, the S. Frederick's cognitive reflection test (CRT), the diagnostic technique Emotional Intelligence (EQ) by N. Hall and S. Epstein's Intuitive Style Questionnaire (REI). On a sample of 105 adolescents (mean age 14.8 years; SD = 1.22; 76 % female), various significant relationships were found between the components of information competence, suggesting the success of activities (“knowledge” and “skill”) on the one hand and scales of verbal, non-verbal and mathematical intelligence, critical thinking, cognitive reflection and emotional intelligence, on the other hand. Digital competence is statistically more significantly associated with the verbal type of intellectual abilities, as opposed to mathematical and non-verbal intelligence. An analysis of the correlations between intellectual abilities and areas of digital competence has shown that the closest links are found in the sphere of consumption of goods and services across the Internet and work with content on the network. The special role of emotional intelligence and cognitive reflection as important cognitive foundations of digital competence is discussed. Thus, the results of the study make it possible to overcome the lack of empirical evidence of the relationship between digital competence and intelligence, focusing on individual components of digital competence that are most associated with intelligence, and on individual intellectual abilities of adolescents that are most associated with successful digital socialization.
Exercise becomes Music: Creating communication through new music experiences using exercise data

This is a new communication proposal using data acquired by Physiological signal measurement. Since the COVID-19 pandemic, opportunities for exercise have been decreasing as people have fewer opportunities to go outside. For this reason, we have created several exercise guidance contents that can be used on a daily basis. The data obtained from the exercise guidance contents can be used in various ways. For example, in a physical education class, the children were motivated to improve their exercise by seeing the actual data of their exercise. There are three exercise guidance contents, each of which has its own feedback loop of acquiring data and utilizing it. "Exercise becomes Music" is an attempt to use them in a more meta way to motivate people to exercise, and to create a larger feedback loop throughout. The data acquired from the exercise guidance content is converted into music that makes the most of the characteristics of each content, and the content is designed to be played by the experiencer while combining the generated music. The surprise of having one's own data converted into music, and the experience of playing with the combination of data, will create feedback for each exercise, and we also hope that sharing the combined music online will create new communication.
Computer vision-based method of pre-alignment of a channel optical waveguide and a lensed fiber

The work is devoted to a technique of pre-alignment of a lensed fiber and a channel waveguide of a photonic integrated circuit using computer vision methods. The design and main units of a machine vision system with illumination of the adjusted objects in reflected light are described. The technique includes detection of the spatial position of the end face of the photonic integrated circuit, fixed at an angle of 90 ± 1° to the horizontal axis of the frame, detection of the coordinates of the end face of the lensed fiber, and the subsequent correction of the position of the lensed fiber using a manipulator system. We propose a method of searching and selecting a single line corresponding to the end face of a photonic integrated circuit using a Hough transform; methods for grouping discontinuous contours of the lensed fiber and true contour determination. These methods are based on a priori knowledge of the lens geometry. Also, we describe options for suppressing noise and overcoming various defects in images. It has been shown experimentally that the error of angle determination of a lensed fiber depends on the distance between the lens and the end face of the photonic integrated circuit. The presented technique makes it possible to determine the longitudinal and angular displacements between the fiber lens and the end face of the photonic integrated circuit with errors less than 4 μm and 0.05°, respectively.
Detection and Logging Changes in Web Pages

Detecting changes in web pages is a process that has many uses for regular users and developers alike. This paper lists and explains the usual steps in the development of one tool that detects changes. Two tools are implemented and tested on real-life web pages. Changes that can be detected in these tools are based on DOM (Document Object Model) trees. Results for two methods of tree comparisons are presented and discussed.
Additional Professional Education of Designers of Sewing Products at the University

The article discusses additional professional education of designers of sewing products at the university, current trends, the structure of continuing professional education, conducted a survey in the form of a survey in the K.G. Razumovsky Moscow State University of Technologies and Management (the First Cossack University), students of the 2nd year of study, specialty 44.03.02 - "Psychological and pedagogical education" and 29.03.05 "Designing of light industry products" about additional professional education, how they are ready to study throughout their lives and improve their qualifications, a comparison of the answers of different areas of training is given.
Predicting Asymptotic Behavior of Network Covert Channels

The problem of covert communication via computer systems is almost as old as the problem of computer security itself. In the earliest years, covert communication was seen as mainly as a theoretical problem. But as computer systems have become more complex and ubiquitous, covert communication has begun to see practical use, particularly in the last two decades (see, e.g. Mazurcyk et al. in [MW19].) In this talk I will be reporting on the work we have been doing at NRL on evaluating the impact that existing research on the asymptotic behavior on covert channels has on embeddings in real-world channels.
CAMERO: Consistency Regularized Ensemble of Perturbed Language Models with Weight Sharing

Model ensemble is a popular approach to produce a low-variance and well-generalized model.However, it induces large memory and inference costs, which are often not affordable for real-world deployment.Existing work has resorted to sharing weights among models.However, when increasing the proportion of the shared weights, the resulting models tend to be similar, and the benefits of using model ensemble diminish.To retain ensemble benefits while maintaining a low memory cost, we propose a consistency-regularized ensemble learning approach based on perturbed models, named CAMERO.Specifically, we share the weights of bottom layers across all models and apply different perturbations to the hidden representations for different models, which can effectively promote the model diversity.Meanwhile, we apply a prediction consistency regularizer across the perturbed models to control the variance due to the model diversity.Our experiments using large language models demonstrate that CAMERO significantly improves the generalization performance of the ensemble model.Specifically, CAMERO outperforms the standard ensemble of 8 BERT-base models on the GLUE benchmark by 0.7 with a significantly smaller model size (114.2Mvs. 880.6M).
Unlock the secrets of letters

Spot visual patterns in the world's written languages to boost the science of graphical communication, says Layal Liverpool
A survey of architectures of neural network accelerators

Nowadays, with the growth in data demand and the improvement in hardware computing power, artificial intelligence (AI) can be applied to a wide range of applications. Among them, neural network algorithms have successfully solved some practical problems, such as face recognition and autonomous driving. Although these algorithms perform well, their computing performance on traditional hardware platforms is still inefficient. To address the issue, some customized accelerators are proposed. In this survey, we introduce some architecture designs of typical accelerators, including the computing unit, data flow, the characteristics of different neural networks to be accelerated, and design considerations on emerging platforms. Finally, we also provide our insights of future trend of neural network accelerators.