Reviewerly/exHarmony huggingface dataset:
Rancang Bangun Plotter 2D Mini Berbasis Arduino Nano

Abstract CNC (Computer Numerical Control) is a machine tool automation system that is operated by abstract programmed commands and a computer-driven control machine that produces very high precision. With this tool, for problems in making logo printing, which previously could be said to be a fairly expensive business because the equipment costs were high enough, it made it difficult for home industries and new entrepreneurs to enter the business. Testing data retrieval using different textures such as plastic, paper, cloth, and leather obtained quite satisfactory results with a percentage of 90%, Thickness testing on objects that require the height of the pen position to be adjusted according to the upper surface of the object in this experiment, the size is not a problem. The imaging process saw success at 89%. in different types affect the results of the drawing, especially on the cloth using the type of pen having a rough eye and a diameter that is too small like a twin pen, the estimated success is 65% on leather and cloth. If you use a pen with a diameter of 1.0mm-1.2mm, the success rate is 90%, but if the pen is inked with a marker it causes the ink to spread everywhere, making it 80% successful. If on paper and leather the results are 90% with any type of pen.
Selection of Online Network Traffic Discriminators for on-the-Fly Traffic Classification

There are several techniques to select a set of traffic features for traffic classification. However, most studies ignore the domain knowledge where traffic analysis or classification is performed and do not consider the always moving information carried in the networks. This paper describes a selection process of online network-traffic discriminators. We obtained 24 traffic features that can be processed on the fly and propose them as a base attribute set for future domain-aware online analysis, processing, or classification. For the selection of a set of traffic discriminators, and to avoid the inconveniences mentioned, we carried out three steps. The first step is a context knowledge-based manual selection of traffic features that meet the condition of being obtained on the fly from the flow. The second step is focused on the quality analysis of previously selected attributes to ensure the relevance of each one when performing a traffic classification. In the third step, the implementation of several incremental learning algorithms verified the usefulness of such attributes in online traffic classification processes.
E-learning in higher education in times of pandemic: A bibliographic review

The research is a bibliographic review on e-learning at the higher level in times of pandemic, e-learning. Databases were reviewed Eric, Scopus, Web of science, Dialnet, Scielo, Google scholar, digital books Likewise, the article aims to explain higher education and the pandemic, the conceptualization of e-learnig, b-learnig, connectivism theory, pedagogical dimensions of the E -Learnig, online activities, and the educational inequalities in times of pandemic. The review of this literature allowed us to reach the following conclusions: e-learning are applications and tools that rely on ICT to facilitate learning, from a pedagogical approach through e-learning, students learn autonomously and independently. The impact on higher education has been dramatic and transformative, and a common trend in education systems around the world has been to respond to the pandemic with “emergency e-learning” protocols, marking a rapid transition from face-to-face classes to learning systems online.
A Survey On Application Of Information Retrieval Models Using Nlp

Text mining accustomedestablish the hidden patterns and to get a vital data fromthe great deal of unstructuredknowledgeit’s going to written material also. Different field techniques like machine learning, visualization, text analysis, database technology, statistics, knowledge management, natural language processing are incorporated in text mining. It’s thought about to be mostly booming area next to big data and artificial intelligence. There exist varied techniques, tools and applications in text mining. Information retrieval thought-about being aeminent application of text mining. Information retrieval could be a key technology for knowledge management. Information retrieval is the method ofgetting and presenting additionalconnected data from the biggest assortment of knowledge resources in step with to the user’s need.This paper is a survey of discussing concerning varied application of Information Retrieval using NLP and available methods of information retrieval. This survey discussesconcerningassorted models used in Information retrieval. Here we tend to conjointlylist the research contribution and limitations of Information retrieval modelsfound in various articles.
Sentiment Analysis Of News On Human mind Using NLP and Deep Learning Approach

The chronic nature of the coronavirus disease (COVID-19) outbreak in late 2019 has brought several devastations to the world. The lack of success in treatment and cure is creating an environment that is crucial for mental wellbeing. The pandemic situation has spread across the globe. This situation has infected more than ten million people and it has disrupted various businesses in the world. The physical distancing and the use of protective masks were suggested, but still, the cases seem to rise. This led to worldwide lockdown in different phases across the world. We have extracted and classified sentiments and emotions from news channel headlines about the coronavirus disease (COVID-19). The headlines considered were those carrying keyword coronavirus news sources. The headlines are classified into positive, negative, and neutral sentiments. The text unbound polarity at the sentence level score is calculated by incorporating the valence shifters.This paper investigates the impact of news on the human mind using sentiment analysis. The research follows a deep learning mechanism to achieve the goal. Deep learning deals with a very large amount of data efficiently and performs the complex task. Deep learning is built explicitly for dealing with a significant amount of large numbers that perform complex tasks which in turn perform automatic learning that is necessary. In this paper, we perform a sentiment analysis of news data during the pandemic lockdown. We have taken a time of two and four weeks after the lockdown was imposed. Investigating the sentiments of people in the form of positive, negative, and neutral news would assist us in determining how people are dealing with the pandemic and its effects on their psychological levels. The study will be effective in the determination of people’s mental well-being. Also, this will be useful in devising appropriate lockdown strategies and crisis management in the future.
Comparison of search algorithms in two-stage neural network training for optical character recognition of handwritten digits

Neural networks are a powerful machine learning technique that give a system the ability to develop multiple levels of abstraction to interpret data. Such networks require training to develop the neural layers and neuron weights in order to form reasonable conclusions from the data being interpreted. The conventional training method is to use backpropagation to feed the error between a neural network’s actual output and desired output back through the neural network to adjust the neural synapses’ weights to minimize such errors on subsequent training data. However, backpropagation has several limitations to its effectiveness in training neural networks. The most relevant limitation of backpropagation is that it tends to become trapped in local optimum solutions. This research studied the effectiveness in using search algorithms to improve upon a feed-forward Multi-Layer Perceptron artificial neural network trained by backpropagation in classifying handwritten digits. The search algorithms used for this research were the Bare Bones Fireworks Algorithm (BBFWA), Canonical Particle Swarm Optimization (PSO), and Cooperative Particle Swarm Optimization (CPSO) algorithms. Two sets of parameters for the BBFWA were tested in this study in order to examine the effects of parameters on the algorithm. The handwritten digit classification data was carried out on the MNIST handwritten character database, a common benchmark for handwritten character recognition. A neural network was trained with backpropagation, and then the search algorithms were seeded with its weights so that they could search for better neural network weight configurations. The complexity of using images of handwritten characters with a feed-forward Multi-Layer Perceptron resulted in a high degree of dimensionality in the problem, which negatively impacted the Particle Swarm Optimization algorithms. To analyze the impact of the problem dimensionality, the neural network was also tested with a PCA compressed MNIST database. It was found that the BBFWA performed the best out of the three algorithms on both datasets, as it was able to consistently improve upon the performance of the original neural networks. Between the two sets of BBFWA parameters, the simulation results indicated that the second parameter set outperformed the first parameter set in terms of both classification accuracy and fitness trends.
Improving the usability and scalability of FINN, a DNN compiler for FPGAs

FINN is a framework developed by Xilinx Research Labs that compiles Deep Neural Network software descriptions into fast and scalable dataflow architectures for inference acceleration on FPGAs. The dataflow architectures are network dependent, sized according to the user-defined throughput requirements, and constrained by available resources on the user-specified FPGA board. Synthesising large neural network designs with a high degree of configurability leads to large build times, spanning from hours to days, to build an entire network. Thus, the first objective of this thesis is to explore and propose a modified FINN accelerator construction methodology that can substantially reduce the build times. The main idea behind our proposal is to reduce the granularity of the architecture to reduce the size of synthesis jobs and to enable logic reuse within and across neural network layers. Using this method, up to 12× speedup in High-Level Synthesis times and up to 2× speedup in end-to-end build times of accelerator networks are achieved. The second limitation that this work addresses relates to the performance scalability of FINN generated architectures. There are two modes of parallelism in FINN that currently provide performance scaling in convolution operations. The first factor, which modifies the number of Processing Elements (PEs), parallelises along the input channels of a convolutional layer and the second factor, that modifies the number of Single Instruction Multiple Data (SIMD) lanes present in each PE, parallelises along the number of output channels of the convolution. Computations are currently not parallelisable across the non-depth dimensions of images, i.e., the side containing pixels of images that faces the viewer. This limitation can restrict the achievable performance for networks that contain layers with large image dimensions and shallow depth dimension. The second part of this work leverages the fine-grained construction methodology to augment FINN performance scaling. The proposed approach introduces a generic FINN modification that enables pixel-level parallelism, i.e., multiple output pixels of a convolutional layer can be processed simultaneously by performing Multiple Matrix Vector (MMV) multiplications at the same time. Using this generic method, MMV number of pixels can be processed simultaneously, an MMV times throughput increase can be obtained at the cost of less than MMV × additional resources.
Effective Enhancement Of Underwater Images Using Multiscale Fusion Technique

Underwater image enhancement is an indispensable area of marine engineering research work. Underwater images typically suffer from degradation problems like low contrast, color variations, blurring, non-uniform lighting, poor visibility conditions, etc. Image enhancement is an important preprocessing step in many computers vision and graphics applications. Generally, underwater images acquired by the camera will be noisy and hazy because of refraction, absorption, and scattering effects of light. Wavelength contingent attenuation of light can be observed in deep water. Thus, the color spectrum of light with a larger wavelength attenuates more when compared to other colors spectrum of light. The major purpose of the image enhancement mechanism is to process the degraded images and results in improving the information content and tweaks visual impact. This paper is based on a multi-scale fusion-based framework with weight maps is manifested to upgrade the quality of shaded and hazed underwater imagery.
The Effect of ICT & Digital Supply Chain (DSC) in Higher Education Institutions

The study identifies the effect of the implementation of information and communication technology-driven digital supply chain (ICT-DSC) model in education institutes. Besides that, the study defines the integration and evaluation criteria of the Digital Supply Chain model in the academic industry. Finally, an integrated model for educational institutes in Bangladesh is developed through the study. This ICT application-based DSC model would formulate a requirement roadmap to express a guideline for the educational technology impact. It would fulfil all relevant stakeholders desire to diminish risks and increase understanding to overcome drawbacks. The study would also propose a systematic methodology that initiates a considerate process for all relevant stakeholders within an academic institution . Technology Acceptance Model (TAM) was adopted as a theoretical framework. To conduct the study, primary data have been collected from 276 stakeholders of academic-industry in Bangladesh who were interviewed about their interest and performance ICT-DSC in educational institutions. As a statistical tool, SPSS was used for pilot test and PLS for data analysis. To elaborate on the current ICT-DSC execution in the academic infrastructure of Bangladesh. Also, this research outcome is to produce an integrated theoretical model for educational institutions in Bangladesh. This is one of the most recent research studies on academic institutions in Bangladesh.
A Comparison of Two Scaling Techniques to Reduce Uncertainty in Predictive Models

This research examines the use of two scaling techniques to accurately transfer information from small-scale data to large-scale predictions in a handful of nonlinear functions. The two techniques are (1) using random draws from distributions that represent smaller time scales and (2) using a single draw from a distribution representing the mean over all time represented by the model. This research used simulation to create the underlying distributions for the variable and parameters of the chosen functions which were then scaled accordingly. Once scaled, the variable and parameters were plugged into our chosen functions to give an output value. Using simulation, output distributions were created for each combination of scaling technique, underlying distribution, variable bounds, and parameter bounds. These distributions were then compared using a variety of statistical tests, measures, and graphical plots.
Robust Classification Combined with Robust out-of-Distribution Detection: An Empirical Analysis

Recently, out-of-distribution (OOD) detection has received considerable attention, because confident labels assigned to OOD examples represent a vulnerability similar to adversarial input perturbation. We are interested in models that combine the benefits of being robust to adversarial input and being able to detect OOD examples. Furthermore, we require that both in-distribution classification and OOD detection be robust to adversarial input perturbation. Several related studies apply an ad-hoc combination of several design choices to achieve similar goals. One can use several functions over the logit or soft-max layer for defining training objectives, OOD detection methods and adversarial attacks. Here, we present a design-space that covers such design choices, as well as a principled way of evaluating the networks. This includes a strong attack scenario where both in-distribution and OOD examples are adversarially perturbed to mislead OOD detection. We draw several interesting conclusions based on our empirical analysis of this design space. Most importantly, we argue that the key factor is not the OOD training or detection method in itself, but rather the application of matching detection and training methods.
Image Tamper Detection Based on Logistic Map and Block Average

Intruders can tamper the medical images while it is on transit in internet. So, various tamper detection techniques came into existence. Mainly the earlier research on watermarking focused on embedding the watermark, and extracting the water mark. Recently, tamper detection is also considered as a necessary task for any efficient watermarking mechanism. This article proposes a tamper identification scheme using the concept of blocks. In a block of 4×4 pixels, there exists 16 pixels, their LSBs are set to zeroes. Then the mean pixel value of a block is enumerated. These 8 binary bits of mean value is hidden in LSBs of upper half (UH) of the block. Furthermore, an 8-bit key value is generated from logistic map (LM). The binary value of block mean and key value are XORed to compute the watermark bits (WBs). These bits are hidden in LSBs of the lower half (LH) of the block. At the receiver side the block mean is computed and made XOR operation with the 8-bit key value created from LM. These are the computed WBs(CWBs). The 8 bits from the LSBs of the pixels of UH of the block are extracted. WBs are extracted from the LH of the block. If the CWBs are same as the extracted WBs and computed block mean is same as the extracted bits from UH of the block, then the block is earmarked as not tampered. The merit of this methodology is enumerated by various quality parameters. The values of these parameters are improved as compared to the related existing literature.
FEDS - Filtered Edit Distance Surrogate

This paper proposes a procedure to train a scene text recognition model using a robust learned surrogate of edit distance. The proposed method borrows from self-paced learning and filters out the training examples that are hard for the surrogate. The filtering is performed by judging the quality of the approximation, using a ramp function, enabling end-to-end training. Following the literature, the experiments are conducted in a post-tuning setup, where a trained scene text recognition model is tuned using the learned surrogate of edit distance. The efficacy is demonstrated by improvements on various challenging scene text datasets such as IIIT-5K, SVT, ICDAR, SVTP, and CUTE. The proposed method provides an average improvement of \(11.2 \%\) on total edit distance and an error reduction of \(9.5\%\) on accuracy.
포괄안보 시대 한국의 해외파병 발전방안 연구

After the Cold War, the concept of security expanded from the military field to the non-military field. Along with this, international peace a ctivities by the United Nations and multinational forces have also increased. Overseas dispatch is an important field of defense diplomatic activity and a means to achieve national (defense) goals.This study analyzes cases of Korean overseas dispatch troops in the post-Cold War era, and suggest developement plans such as cooperation between the Ministry of National Defense and the Ministry of Foreign Affairs, promoting the advancement to high-ranking positions in the United Nations and UN peacekeeping missions, building a practical cooperation system during the period of dispatch, improvement of the expertise of overseas troops, taking the risk of overseas distpatches, and construction of a control center and PKO center at the national level.
The Analysis of Correlative Conjunctions Used in Undergraduate Students’ Research Articles

This research belongs to qualitative research. The aims of this research are to identify the types of correlative conjunction and to explain their functions in students’ research articles. The data source of this research is students’ research articles written by undergraduate students of English Language Education Program at Purworejo Muhammadiyah University in the academic year of 2018/2019. In collecting the data, the researcher checked all the research articles published from August 2019 until December 2019. In analyzing the data, the researcher organized the data from the first correlative conjunction until the last correlative conjunction found in research articles; gave code for each of the correlative conjunctions; classified the correlative conjunctions based on the types: both__and, not only__but also, either__or, and neither__nor; classified the correlative conjunctions based on their functions in joining sentence elements such as words, phrases, and clauses; counted the data; discussed the finding; and drew conclusion and suggestion based on the data in this research. The result of this research shows that there are 91 correlative conjunctions found in 46 research articles: 39 both__and (42.86%), 40 not only__but also (43.96%), 8 either__or (8.79%), and 4 neither__nor (4.39%). There are 25 conjunctions connecting words (27.47%), 65 conjunctions connecting phrases (71.43%), and 1 conjunction connecting clauses (1.10%).
Research on the Media Image Construction of “People’s Daily” on Alzheimer’s Disease

The "China Statistical Yearbook 2020" issued by the National Bureau of Statistics shows that China's elderly population aged 65 and above has reached 176 million in 2019, accounting for 12.6% of the country's total population, realizing the transition to an aging society.[4] Solving the problem of aging is an important issue for China's development in this century, and it is very important to scientifically and effectively deal with the rapid development of the aging population in the middle of this century.The Alzheimer's Disease China (ADC) believes that the neurodegenerative cognitive disorder represented by Alzheimer's disease has become a part of the aging problem of Chinese society and needs to be solved.In China, the number of Alzheimer's patients in the country reached approximately 10 million in 2018.The public has misunderstandings about Alzheimer's disease, and it is imperative to increase the social attention of Alzheimer's disease.The study selects relevant reports on Alzheimer's disease since the inception of the People's Daily.Based on the news frame theory, it uses content analysis to conduct high-, middle-, and low-level research.The high-level research includes report time, report theme, report genre, and length of the report, the middle-level research includes news source and emotional tendency, and the low-level research performs word frequency analysis.It draws conclusions through statistics and analysis, analyzes and summarizes the relevant reports of "Alzheimer's disease" in "People's Daily" for the Chinese media to learn from, and puts forward reasonable suggestions for its shortcomings.
Research Guides: Critical Evaluation of News Sources: Websites for Evaluating News

Learn how to determine the credibility of news articles and sites, understand the importance of journalistic integrity, and perform basic fact-checking measures.