Reviewerly/exHarmony huggingface dataset:
Efforts to Improve the Skills of Viewing Fables Using Fantasy Wayang Media in Class I SDN 2 Arjasari

&lt;p&gt;&lt;em&gt;This research is motivated by low listening skills, especially in fable stories. This study aims to improve listening skills to fable stories using fantasy puppet media. This type of research uses PTK with the first cycle using fable story “Friendship of the Lion and the Rat”, second cycle using the story of the fable “The Greedy Crocodile”. The technique of collecting data was by seeing the class teacher learning without using fantasy puppet media and giving 10 PG questions. The results showed an increase in listening skills the first cycle the number of students who received a complete score was 4 students with a percentage of 57% completeness, in the second cycle the number of students who received a complete score was 7 students with a percentage of 100% completeness. It was concluded that the fantasy puppet media used in reading fable stories could improve students listening skills.&lt;/em&gt;&lt;/p&gt;
Implementing voice communication technology in patient applications

AstraZeneca as a biopharmaceutical company performs clinical trials on human volunteers every day. It is crucial that these participants complete the trial to maximise the medical learning from the ...
One coin – Scam cryptocurrency impact on Financial Market

In today we view a Scammers companies are evolving; however, Technology development and cryptocurrencies propagation, digital acceptance currencies by participants is a golden key in the hands of scammers companies, notwithstanding the companies Use a new cryptocurrencies name and new tags for it, many people and fans of Digital currencies fall with this scams network, with the reason for this scamming was they did against crypto buyer and seller, It caused a non-positive label for cryptocurrency.
Development of Surrounding Environment Recognition System for Small Unmanned Aircraft

When an unmanned aircraft autonomously flies in an extreme environment such as a disaster site, it is necessary to cope with environmental changes such as the influence of disturbance and the movement from outdoors to indoors. In this research, we are aiming to develop supervisor-type navigation and control system for unmanned aircraft to adapt to various surrounding environments. The research objective of this paper is to develop a system that recognizes the surrounding environment, which is one of the functions of the supervisor. By knowing the state of the environments where the unmanned aircraft is flying by environment recognition system, it is possible to issue an instruction for correction of the sensor configuration and control model to the navigation system and control system. In this paper, we first design deep learning models such as feed forward neural network(FFNN) or long short-term memory (LSTM) using LIDAR and GNSS sensor data for environment recognition. Designed system is verified by real time environment recognition experiment. From experiments, it was confirmed that ambient environment recognition can be performed with a F1-measure of about 0.94 using sensor data of LIDAR and GNSS. Moreover, it is considered that the performance of the surrounding environment recognition system can be further improved depending on the structure of the learning model and the training data used for deep learning.
정치 이벤트에 대한 신문보도사진 연구 - 역대 남북정상회담 최고통치자 사진을 중심으로

Korean summit were framed and reported according to the newspaper’s reporting tendency. The subject of analysis is the Chosun Ilbo, a conservative newspaper, and the Hankyoreh, a progressive newspaper. A total of 161 cases were analyzed in which two top rulers appeared among the four report photos related to the inter-Korean summits during the Kim Dae-jung, Roh Moo-hyun and Moon Jae-in administrations. As a result of the study, the media’s ideological faction that appeared in general newspaper articles was also shown in the press photos. The two newspapers sympathized with the creation of a peaceful atmosphere on the Korean Peninsula during the first Pyongyang summit in 2000, which was held for the first time after the division of the two Koreas. However, during the 2nd, 3rd, and 4th inter-Korean summits, the faction between the two newspapers became clearer. It was analyzed that the Chosun Ilbo published more photos and reports than the Hankyoreh during the first meeting, but after that, it reported about three times less than the Hankyoreh. The Chosun Ilbo used relatively less friendly and defensive framing than the Hankyoreh when selecting, excluding, and posting photos, and the Hankyoreh was more friendly and more aggressive than the Chosun Ilbo. This means that the press photos also confirmed that ideological symbiotic relations or political parallelism according to the media tendencies are at play. Based on the results of this study, the discussion was focused on a plan to expand the search area of photojournalism.
Adaptive Retrieval Method of Legal Information based on Artificial Intelligence

In the current legal information retrieval methods, keywords are used for extraction and neural networks are used for search. However, the keywords of cases in legal information are different. Simply using keywords to extract the search results in insufficient accuracy. Therefore, an adaptive retrieval method of legal information based on artificial intelligence is proposed. First, establish a positional legal language model to determine the total number of spreads in the term. According to the definition of the term in the model, calculate the similarity of the semantic concept of the term. After confirming that the concept is similar to the term, calculate the co-occurring words in the term. Finally, an artificial intelligence algorithm is used to retrieve the co-occurring words. In order to verify the feasibility of the designed retrieval method, literature [1], literature [2] and design methods were used to retrieve the contents of a certain legal information database. The experimental results show that the designed retrieval method has a higher retrieval accuracy and meets the design requirements.
Generative modeling with one recursive network

We propose to train a multilayer perceptron simultaneously as an encoder and a decoder in order to create a high quality generative model. In one call a network is optimized as either an encoder or decoder, and in a second recursive call the network uses its own outputs to learn the remaining corresponding function, allowing for the minimization of popular statistical divergence measures over a single feed-forward function. This new approach derives from a simple reformulation of variational bayes and extends naturally to the domain of Generative Adversarial Nets. Here we demonstrate a single network which learns a generative model via an adversarial minimax game played against itself. Experiments demonstrate comparable efficacy for the single-network approach versus corresponding multi-network formulations.
AutoCleansing: Unbiased Estimation of Deep Learning with Mislabeled Data

Mislabeled samples cause prediction errors. This study proposes a solution to the problem of incorrect labels, called AutoCleansing, to automatically capture the effect of incorrect labels and mitigate it without removing the mislabeled samples. AutoCleansing consists of a base network model and sample-category specific constants. Both parameters of the base model and sample-category constants are estimated simultaneously using the training data. Thereafter, predictions for test data are made using a base model without the constants capturing the mislabeled effects. A theoretical model for AutoCleansing is developed and showing that the gradient of the loss function of the proposed method can be zero at true parameters with mislabeled data if the model is correctly constructed. Experimental results show that AutoCleansing has better performance in test accuracy than previous studies for CIFAR-10, CIFAR-100, SVHN, and ImageNet datasets.
Readers' Reception of Satin Merah Detective Novel as a Literary Learning Material in Senior High School

Literary learning materials are one of the important components used by teachers in learning activities. Good learning materials can help students achieve learning goals. Sources of literature learning materials tend to focus on reference books from the government. Even though there are still many literary learning materials that can be used, one is novel. The research objective was to describe the teacher's response to Satin Merah's novel by Brahmanto Anindito and Rie Yanti as literary learning material at Senior High School. This research uses a pragmatic approach. The research is based on the reception of Indonesian language teachers at the Senior High School level on the feasibility of a novel based on the feasibility assessment of the BSNP in terms of content, language, and presentation. This study indicates that Satin Merah's novel's feasibility level is 82.5%, which means that this novel is feasible and has a good reputation as literary learning material at Senior High School. The feasibility results are shown in terms of content feasibility of 84.2%, language feasibility of 81.5%, and presentation feasibility of 82%. In terms of content, this novel develops self-potential, has creativity and depth of story, and develops readers' characters to love and preserve local culture. In terms of language, the language used is following Indonesian rules and according to the target audience. In terms of presentation, this novel pays attention to the building blocks and mostly develops characters and inspires readers.
Smart Contract Security: a Practitioners' Perspective

Smart contracts have been plagued by security incidents, which resulted in substantial financial losses. Given numerous research efforts in addressing the security issues of smart contracts, we wondered how software practitioners build security into smart contracts in practice. We performed a mixture of qualitative and quantitative studies with 13 interviewees and 156 survey respondents from 35 countries across six continents to understand practitioners' perceptions and practices on smart contract security. Our study uncovers practitioners' motivations and deterrents of smart contract security, as well as how security efforts and strategies fit into the development lifecycle. We also find that blockchain platforms have a statistically significant impact on practitioners' security perceptions and practices of smart contract development. Based on our findings, we highlight future research directions and provide recommendations for practitioners.
RAK ACCESSORIES FUNNY

&lt;p&gt;Student creativity program in this entrepreneurship class, we run a business program of making cute accessory shelves from used cardboard. This used cardboard is very easy to get at the nearest shops or buy and sell used goods and the price is also quite affordable. Besides being easy to get, used cardboard is also easy to make several forms of crafts, one of which is a shelf. The shelf that we are going to make is a small shelf model where this shelf is made in various shapes, which will make many people like these models. Here what we need in addition to tools and materials is that we need skills and strong creative power. So that the shelves that we will create can attract consumers to buy. In the community, there are many unique shelves that make consumers want to buy them, but the price is a little less affordable for consumers' wallets. Therefore we take advantage of this situation to make cute accessory racks in order to help consumers more comfortable arranging their accessories or makeup with hemp by using this cute accessory rack that we made. The form of our business is that it is still in an individual stage and indeed this business is still new and is still trying to enter the business world. But we already have our own business brand, namely "RASEL" which means Cute Accessories Rack. In the future, if this business can get extraordinary profits and have enough customers, then we will increase this business to be even bigger.&lt;/p&gt;
Implementation of Artifical Neural Networks with Multilayer Perceptron for Analysis of Acceptance of Permanent Lecturers

Lecturer acceptance selection is the first step in building an education. The Multilayer Perceptron method can be applied in the case of permanent lecturer admissions. The problem faced in the admission of permanent lecturers is that reception is still subjective. This research will prove the ability of the Multilayer Perceptron algorithm to classify eligibility as a lecturer or not. Inputs from this study were prospective applicants' data, namely age, grade point average (GPA), written test score, interview value, and home base status. Sample data amounted to 100 data. 75% of the data is used as training data, and 25% as test data. The test results of the accuracy of the data are known that the multilayer perceptron neural network method has an accuracy rate of 98.7% and with a ROC Area value of 0.989. This proves that the application of the model used belongs to the classification category very well because it has a ROC value between 0.90-1.00.
제조기업구성원의 디지털전환(DX) 인식이 디지털기술 수용에 미치는 영향

Purpose The current research aims to build base data for digital transformation(DX) of manufacturing companies and to develop guidelines to apply...
Signal Propagation in a Gradient-Based and Evolutionary Learning System

Generative adversarial networks (GANs) exhibit training pathologies that can lead to convergence-related degenerative behaviors, whereas spatially-distributed, coevolutionary algorithms (CEAs) for GAN training, e.g. Lipizzaner, are empirically robust to them. The robustness arises from diversity that occurs by training populations of generators and discriminators in each cell of a toroidal grid. Communication, where signals in the form of parameters of the best GAN in a cell propagate in four directions: North, South, West, and East, also plays a role, by communicating adaptations that are both new and fit. We propose Lipi-Ring, a distributed CEA like Lipizzaner, except that it uses a different spatial topology, i.e. a ring. Our central question is whether the different directionality of signal propagation (effectively migration to one or more neighbors on each side of a cell) meets or exceeds the performance quality and training efficiency of Lipizzaner Experimental analysis on different datasets (i.e, MNIST, CelebA, and COVID-19 chest X-ray images) shows that there are no significant differences between the performances of the trained generative models by both methods. However, Lipi-Ring significantly reduces the computational time (14.2%. . . 41.2%). Thus, Lipi-Ring offers an alternative to Lipizzaner when the computational cost of training matters.
Analysis of the Noise Reduction Characteristics of a Diffuser with Fizeau Interferometry

To solve the coherent noise problem of an interference image, the method of a rotating diffuser was adopted to change the coherence of a beam to reduce the noise of the interference system. The relationship between the speed of the diffuser and the signal-to-noise ratio (SNR) of the fringe contrast system was simulated to obtain the diffuser control parameters needed for the best interference fringe state. The fringe contrast of each image and the SNR of the system were analyzed. The results showed that the increased speed of the diffuser reduced the contrast of the interference image to a certain extent, but the increased speed also effectively improved the SNR and facilitated the subsequent interference image processing. Due to the coherent noise in the interferometric system, the method of the rotated diffuser reduced the coherence of the light beam to suppress the noise of the interference image. By analyzing the coherent noise reduction characteristics of the rotated diffuser with different surface roughnesses, the relationship between the surface roughness and the noise contrast for different rotation speeds was simulated, and the effective roughness range with the noise reduction effect was selected. A noise reduction system was built based on Fizeau interference, and the noise contrast of the interference image was collected and calculated. The effective range of σh/λ was 0.2–0.5 when the rotation speed was 10 r/s, while the effective range of σh/λ was 0.4–0.6 when the rotation speed was 100 r/s. The experimental results showed that the surface roughness and wavelength ratio σh/λ of the rotated diffuser increased when the noise contrast tended toward 1, but the effective range of the surface roughness decreased with the increase of the rotational speed of the diffuser.
The Teaching English Reading Skill to Senior Vocasional School Students in Samarinda

Reading is one of the abilities of English lessons, the author tried to explore the reading lessons that carried out at SMK Negeri 15 Samarinda. The thing we wanted to know is what strategies are used to teach reading skill at the school and how to implement it. This research used descriptive qualitative research. The subjects in this study were four English teachers at SMK Negeri 15 Samarinda. The instrument in this research were interviews and documentation. In the section on the results of the findings obtained, it shows that each teacher has a different strategy for teaching students in the classroom and the teacher also uses different implementations teaching technique. Basically, the teacher uses the lesson plan and syllabus and the semester program that has been agreed upon to become teaching material in the classroom.
Model information as an analysis tool in deep learning

Information-theoretic perspectives can provide an alternative dimension of analyzing the learning process and complements usual performance metrics. Recently several works proposed methods for quantifying information content in a (which we refer to as model information). We demonstrate using information as a general analysis tool to gain insight into problems that arise in deep learning. By utilizing information in different scenarios with different control variables, we are able to adapt information to analyze fundamental elements of learning, i.e., task, data, model, and algorithm. We provide an example in each domain that information is used as a tool to provide new solutions to problems or to gain insight into the nature of the particular learning setting. These examples help to illustrate the versatility and potential utility of information as an analysis tool in deep learning.
Manajemen Anestesia pada Evakuasi Epidural Haemorrhage (EDH) dengan Pendarahan Masif

Traumatic brain injury causes mortality and morbidity worldwide. Epidural Haemorrhage (EDH) is a form of head injury where time is an indicator that must be considered in its management. The main focus during traumatic brain injury management is patient stabilization and control of intracranial pressure, as well as maintaining brain oxygenation and perfusion. Subsequently, surgical decompression was performed. Evacuation and bleeding control should be done in a short time to avoid further injury. The practice of neuroanesthesia, as a support in the management of traumatic brain injuries, is often associated with blood loss that results in anemia during the intraoperative and postoperative periods. Although anemia correlates with poor outcome in brain-injured patients, red blood cell transfusion to correct anemia also correlates with poor outcome in patients. There are still no clear recommendations regarding the administration of transfusions, whether restrictive or massive, regarding the benefits provided. Male patient, age 51 years with complaints of decreased consciousness and vomiting, referred from the previous hospital with a diagnosis of moderate head injury 225 with 96cc temporoparietal EDH, 11mm midline shift to the right, and cerebral edema. During the operation period, there was massive bleeding that interfered with the hemodynamic status so that blood components were transfused until a stable hemodynamic status was obtained. In postoperative care in the ICU, the patient is relatively in good condition.
Neuro-algorithmic Policies for Discrete Planning

Although model-based and model-free approaches to learning the control of systems have achieved impressive results on standard benchmarks, generalization to variations in the task are still unsatisfactory. Recent results suggest that generalization of standard architectures improves only after obtaining exhaustive amounts of data.We give evidence that the generalization capabilities are in many cases bottlenecked by the inability to generalize on the combinatorial aspects. Further, we show that for a certain subclass of the MDP framework, this can be alleviated by neuro-algorithmic architectures. Many control problems require long-term planning that is hard to solve generically with neural networks alone. We introduce a neuro-algorithmic policy architecture consisting of a neural network and an embedded time-depended shortest path solver. These policies can be trained end-to-end by blackbox differentiation. We show that this type of architecture generalizes well to unseen variations in the environment already after seeing a few examples. https://sites.google.com/view/neuro-algorithmic
Analysis of the possibilities of using a deep neural networks as an image recognition technology to solve the problem of automating attendance tracking

The task of counting the number of people is relevant when conducting various types of events, which may include seminars, lectures, conferences, meetings, etc. Instead of monotonous manual counting of participants, it is much more effective to use facial recognition technology, which makes it possible not only to quickly count those present, but also to recognize each of them, which makes it possible to conduct further analysis of this data, identify patterns in them and predict. The research conducted in this paper determines the quality assessment of the use of facial recognition technology in images andvideo streams, based on the use of a deep neural network, to solve the problem of automating attendance tracking.