Reviewerly/exHarmony huggingface dataset:
SeismoGen: Seismic Waveform Synthesis Using Generative Adversarial Networks

Detecting earthquake events from seismic time series has proved itself a challenging task. Manual detection can be expensive and tedious due to the intensive labor and large scale data set. In recent years, automatic detection methods based on machine learning have been developed to improve accuracy and efficiency. However, the accuracy of those methods relies on a sufficient amount of high-quality training data, which itself can be expensive to obtain due to the requirement of domain knowledge and subject matter expertise. This paper is to resolve this dilemma by answering two questions: (1) provided with a limited number of reliable labels, can we use them to generate more synthetic labels; (2) Can we use those synthetic labels to improve the detectability? Among all the existing generative models, the generative adversarial network (GAN) shows its supreme capability in generating high-quality synthetic samples in multiple domains. We designed our model based on GAN. In particular, we studied several different network structures. By comparing the generated results, our GAN-based generative model yields the highest quality. We further combine the dataset with synthetic samples generated by our generative model and show that the detectability of our earthquake classification model is significantly improved than the one trained without augmenting the training set.
Multi-Headed Self-Attention-based Hierarchical Model for Extractive Summarization

In this paper, we present a novel model for extractive document summarization using a single layer of multi-headed attention on the top of representations from an RNN (GRU). The model is hierarchical and learns the sentence and document representations hierarchically. It achieves performance better or comparable to the earlier such models. The paper establishes the utility of multi-headed attention in refining the representations and capturing the dependencies of words in a sentence and sentences in a document.
OSEMN Approach for Real Time Data Analysis

Data analysis system is the study of people opinions, sentiments, attitudes, and emotions expressed through written language. Sentiment analysis is just a part of it in this system we compare the accuracy result of two languages sentiment analysis. If we saw than sentiment analysis is one of the most active research areas. It is popular because of two reasons. First, it has a big range of applications because opinions are center of almost all human activities and it shows our behaviours. Whenever we make a decision, we have to heard other’s opinions as well. Second, it presents many challenges research problems, which had never been strive before the year 2000. Part of the reason for the lack of study before was that there was small dogmatic text in digital forms. There is no surprise that the establishment and the rapid growth in the field coincide with the social media on the Web. In fact, the research has also increase outside of computer science to manage science and social science due to its importance to business and society as a whole.Information analysis system is the system in which we measure the accuracy rate of both languages chirps. The main thing is this that this project is a newly formed project. We can say that sentiment analysis is just a part of it for that we have to understand what is sentiment classification and analysis. So Sentiment classification is a way to inspect the personal data in the chirps or data and then extract the opinion. Chirps analysis the method by which information is withdraw from the opinions, and emotions of people in regards to things. During decision taking the opinion of other person shave a drastic effect on users or customers ease because they make choices regarding to e-shopping, choosing events, products, things. The approaches towards chirps analysis work according to a particular level, document level. This paper aims at analysing a solution for the sentiment classification at a powdery, mainly in the sentences in which the polar nature of the chirps or sentences given by three categorization name as positive ,negative and neutral.
Performance evaluation of Compact Prediction Tree algorithm for Web Page Prediction

Web Usage Mining is the process of automatic extraction of user navigation patterns from the Web Log files. Search Engines and e-Commerce websites collect a lot of clickstream data and leverage this technique to provide their users with personalized content. Using traditional web usage mining techniques in an enhanced manner valuable patterns and hidden knowledge can be discovered. Most of the techniques require the use of algorithms like k-Nearest Neighbors and Decision Trees. This paper intends to evaluate the Compact Prediction Tree algorithm based on their accuracy and variation for the next web page prediction purpose.
Quality-Aware Streaming Network Embedding with Memory Refreshing

Static network embedding has been widely studied to convert sparse structure information into a dense latent space. However, the majority of real networks are continuously evolving, and deriving the whole embedding for every snapshot is computationally intensive. To avoid recomputing the embedding over time, we explore streaming network embedding for two reasons: 1) to efficiently identify the nodes required to update the embeddings under multi-type network changes, and 2) to carefully revise the embeddings to maintain transduction over different parts of the network. Specifically, we propose a new representation learning framework, named Graph Memory Refreshing (GMR), to preserve both global types of structural information efficiently. We prove that GMR maintains the consistency of embeddings (crucial for network analysis) for isomorphic structures better than existing approaches. Experimental results demonstrate that GMR outperforms the baselines with much smaller time.
End-to-End Deep ROI Image Compression

In this paper, we present the effectiveness of image compression based on a convolutional auto encoder (CAE) with region of interest (ROI) for quality control. We propose a method that adapts image quality for prioritized parts and non-prioritized parts for CAE-based compression. The proposed method uses annotation information for the distortion weights of the MS-SSIM-based loss function. We show experimental results using a road damage image dataset that is used to check damaged parts and an image dataset with segmentation data (ADE20K). The experimental results reveals that the proposed weighted loss function with CAE-based compression from F. Mentzer et al. learns some characteristics and preferred bit allocations of the prioritized parts by end-to-end training. In the case of using road damage image dataset, our method reduces bpp by 31% compared to the original method while meeting quality requirements that an average weighted MS-SSIM for the road damaged parts be larger than 0.97 and an average weighted MS-SSIM for the other parts be larger than 0.95.
Ação de Comunicação em Saúde no WhatsApp com base no Perfil Digital de Portadores de Hipertensão e Diabetes

Chronic noncommunicable diseases are the cause of a large number of deaths in Brazil and require new approaches tohealth education and communication, including incorporating digital technologies. However, social disparities and access tothese technologies make the design of health education campaigns and interventions a challenge. Therefore, the objective ofthis study was to evaluate the digital profile of SUS users, patients with hypertension and Diabetes Mellitus (DM) to developa health communication strategy considering the profile of these users. 499 individuals were interviewed in Maringa-Paranaand of these 57 were diabetic, 280 were hypertensive and 162 were diabetic and hypertensive. For the creation of content,the principles of design, software for graphic and audio creation, as well as guidelines for the development of health contenton adherence to drug therapy were used. It was observed that 32.87% of the participants belonged to the age group of50 to 59 years old, with 64.93% being female. Regarding the users’ digital profile, 81.56% have social networks, of whichWhatsApp (95.79%) and Facebook (68.54%) were the most popular. The data allowed to define a target audience, personaand vehicle to be used in the intervention (WhatsApp). 58 images, 14 audio files and 49 text messages with health educationcontent were originally produced by the team. The present study pointed to the use of WhatsApp’s multimedia resource witha focus on images and audios that would meet the socioeconomic profile of the target population. Prior knowledge of therecipients’ profile promotes a more assertive and objective communication of health communication campaigns, differingfrom mass communication strategies that are not intended to personalize the message for the various segments of the public.
Effects of Brain Games on Global Cognition among Older Filipinos

Objective. This study ascertained the effects of brain games on global cognition among older Filipinos. Methods. A quasi-experimental design was used. The participants were 60 elderly Filipinos, aged 60 to 93 years, residing in Baguio City, and were not engaging in any physical or nutritional interventions to improve cognitive function. None have subclinical cognitive impairments as screened using the Mini-Cognitive Examination. The Addenbrooke's Cognitive Examination-III (ACE-III) tool which correlated significantly with standard neuropsychological tests, and provided a high internal reliability score of 0.88, was used. Data were analyzed using descriptive and inferential statistics.Results. Pretest mean scores of both active control and experimental groups increased in the posttest. Additionally, both coloring and brain games had extremely significant effects on global cognition (p=0.0001). Lastly, the unrelated t-test revealed extremely significant difference between the effects of brain games and coloring on global cognition (p=0.0001).Conclusions. Based on the results, brain games showed evidence of effectiveness in improving global cognition, and that compared to coloring, it is more effective in improving global cognition among older Filipinos. Variables like motivation and expectancy effects influence the improvement in global cognition among older Filipinos. Lastly, cognitive training using brain games show promise as a non-pharmacologic intervention to address cognitive decline.
Do The Children Need To Learn English at Elementary Level ?

This aim of this research is to provide an insight on English as foreign language learning at elementary school level. Since the English is learned as foreign language in Indonesia, the shifting of English in Indonesia curriculum is one of the interested research focus. It is explicitly declared in Curriculum 2013 that English is taught as an extracurricular subject in Indonesia elementary school. The government does not obligate the elementary students to learn English. The elementary schools have an opportunity to decide English to be included as a subject in the curriculum. The shifting of the government’s consideration in deciding the starting focus of English learning at junior high school level is the maturity level of learners in learning a foreign language. Dealing with the stated focus, a critical overview on the foreign language learning is conducted. Several research reported that learning a foreign language is better to be started at the early age because of the early age students are easier to imitate the new language. The way of thinking in imitating stages is assumed as the best time to start learning a foreign language. In another point, several research reported that maturity level of learners is the essential point to be considered because learning a foreign language need is not only imitating the language but also understanding the culture, combining linguistics aspects and so on. Despite the fact on the different views of scholars, both of the parties agree on the focus of learning a language is not a matter of age. The contributions of several factors are also part of the supported system in learning foreign language. In term of English foreign language learning, different age-level have to be given different learning input. An early age students should be given speaking and listening topics due to the students do not involve the critical thinking process. Meanwhile, the mature students are better to be provided reading and writing topic due to the depth thinking phase is in theirs.
Visualizing Air Drums: Analysis of Motion and Vocalization Data Related to Playing Imaginary Drums

Air drums, or imaginary drums, are commonly played as a form of participating in musical experiences. The gestures derived from playing air drums can be acquired using accelerometers and then mapped into sound control responses. Commonly, the mapping process relies on a peak-picking procedure that maps local maxima or minima to sound triggers. In this work, we analyzed accelerometer and audio data comprising the motion of subjects playing air drums while vocalizing their expected results. Our qualitative analysis revealed that each subject produced a different relationship between their motion and the vocalization. This suggests that using a fixed peak-picking procedure can be unreliable when designing accelerometer-controlled drum instruments. Moreover, user-specific personalization can be an important feature in this type of virtual instrument. This poses a new challenge for this field, which consists of quickly personalizing virtual drum interactions. We made our dataset available to foster future work in this subject.
Data-Driven Spatio-Temporal Analysis via Multi-Modal Zeitgebers and Cognitive Load in VR

Virtual Reality (VR) produces a highly realistic simulation environment to engage users with Immersive Virtual Environments (IVEs). To interact effectively with users, VR builds intensive media through the multi-modal sense functions in the lower level, such as visual, auditory, tactile, and olfactory senses. However, the higher-level perceptions, e.g., the temporal duration, the sense of presence, and the cognitive load are less explored. These higher-level perceptions are part of the critical evaluation criteria for VR design. In this paper, we divide the external zeitgebers into visual and auditory zeitgebers. We then combine these zeitgebers with the attention-oriented cognitive load to investigate their effects on temporal estimation and presence, particularly in IVEs. We propose a data-driven method to build a multi-modal predictive equation for time estimation and presence, in an effort to figure out the essential elements of users' spatial and temporal perception in VR. We also design a complicated application and validate the predictive equation. Our feature-based model is able to guide the VR application design in terms of the subjective time length judgment and presence of users as well as achieve a better VR user experience.
High-Throughput, Resource-Efficient Multi-Dimensional Parallel Architecture for Space-Borne Sea-Land Segmentation

Sea-land segmentation based on edge detection is commonly utilized in ship detection, coastline extraction, and satellite system applications due to its high accuracy and rapid speed. Pixel-level distribution statistics do not currently satisfy the requirements for high-resolution, large-scale remote sensing image processing. To address the above problem, in this paper, we propose a high-throughput hardware architecture for sea-land segmentation based on multi-dimensional parallel characteristics. The proposed architecture is well suited to wide remote sensing images. Efficient multi-dimensional block level statistics allow for relatively infrequent pixel-level memory access; a boundary block tracking process replaces the whole-image scanning process, markedly enhancing efficiency. The tracking efficiency is further improved by a convenient two-step scanning strategy that feeds back the path state in a timely manner for a large number of blocks in the same direction appearing in the algorithm. The proposed architecture was deployed on Xilinx Virtex k7-410t to find that its practical processing time for a [Formula: see text] remote sensing image is only about 0.4[Formula: see text]s. The peak performance is 1.625[Formula: see text]gbps, which is higher than other FPGA implementations of segmentation algorithms. The proposed structure is highly competitive in processing wide remote sensing images.
IMPLEMENTASI ALGORITMA DIJKSTRA DAN METODE HAVERSINE PADA PENENTUAN JALUR TERPENDEK PENDAKIAN GUNUNG MERAPI JALUR SELO BERBASIS ANDROID

Mount Climbing or mountaineering is one of high-risk sports having certain techniques. There are many factors emerging the lost of mountaineer, such as, getting lost or having out of the track because of bad weather and trying for a new track without well-preparations. Meanwhile, the information of mountaineering route usually can be got from brochure or map at mount base camp in general. Furthermore, some cases state that the mountaineers also must find the closest path to the peak or the closest base camp because of bad weather or condition. The lack of mountain cartographic application and the importance of the closest track become the background of the Dijkstra algorithm implementation based on Android for deciding the shortest track to Mount Merapi via Selo. The methods of this research are Dijkstra algorithm for deciding the shortest rate and Harversine method for giving the value of passed node. The use of Google map and GPS applications are used for mapping and finding the location coordinate. This data will be processed using PHP and MySql database. The results of research are this application can be used easily using smartphone and the feasibility test shows “Good” value with 77% average.
EusTimeML: A mark-up language for temporal information in Basque

We present EusTimeML, a mark-up language for temporal information in texts written in Basque. It is compliant with the TimeML specifications, while offering some adapted attributes and attribute values in order to represent the language-specific features of Basque. In particular, alterations have been carried out for verb tense, aspect and modality coding, as well as for time expression and signal annotation. EusTimeML also provides a major extension to the existing TimeML schemes, since the attributes and values for factuality annotation have been added to the existing temporal information annotation scheme. EusTimeML has been used to annotate the EusTimeBank Corpus, the news and history narratives corpus that has been used as the gold standard in temporal information processing in Basque.
Hyperchaos-Based Cryptosystem for Multimedia Data Security

In this paper, we present a software development of multimedia streaming encryption using Hyperchaos-based Random Number Generator (HRNG) implemented in C#. The software implements and uses the proposed HRNG to generate keystream for encrypting and decrypting real-time multimedia data. The used HRNG consists of Hyperchaos Lorenz system which produces four signal outputs taken as encryption keys. The generated keys are characterized by high quality randomness which is confirmed by passing standard NIST statistical tests. Security analysis of the proposed encryption scheme through image and audio security analysis confirms its robustness against different kind of attacks.
Learning Adjustment Sets from Observational and Limited Experimental Data

Estimating causal effects from observational data is not always possible due to confounding. Identifying a set of appropriate covariates (adjustment set) and adjusting for their influence can remove confounding bias; however, such a set is often not identifiable from observational data alone. Experimental data allow unbiased causal effect estimation, but are typically limited in sample size and can therefore yield estimates of high variance. Moreover, experiments are often performed on a different (specialized) population than the population of interest. In this work, we introduce a method that combines large observational and limited experimental data to identify adjustment sets and improve the estimation of causal effects for a target population. The method scores an adjustment set by calculating the marginal likelihood for the experimental data given an observationally-derived causal effect estimate, using a putative adjustment set. The method can make inferences that are not possible using constraint-based methods. We show that the method can improve causal effect estimation, and can make additional inferences when compared to state-of-the-art methods.
Jigsaw-VAE: Towards Balancing Features in Variational Autoencoders

The latent variables learned by VAEs have seen considerable interest as an unsupervised way of extracting features, which can then be used for downstream tasks. There is a growing interest in the question of whether features learned on one environment will generalize across different environments. We demonstrate here that VAE latent variables often focus on some factors of variation at the expense of others - in this case we refer to the features as ``imbalanced''. Feature imbalance leads to poor generalization when the latent variables are used in an environment where the presence of features changes. Similarly, latent variables trained with imbalanced features induce the VAE to generate less diverse (i.e. biased towards dominant features) samples. To address this, we propose a regularization scheme for VAEs, which we show substantially addresses the feature imbalance problem. We also introduce a simple metric to measure the balance of features in generated images.
Dynamic Artificial Potential Fields for Multi-User Redirected Walking

Immersive Virtual Reality (VR) systems that combine Head Mounted Displays (HMDs) and a position tracking system support the multiple users or participants to collaborate in the same physical space for a large-scale virtual environment. Because the multiple users sharing the same physical space are in a dynamic state, the key technique of multi-user VR system is how to solve the problem of potential collisions among the users who are moving both virtually and physically. In order to better solve the collision problem caused by such dynamic changes, this work presents a new strategy of multi-user redirected walking using dynamic artificial potential fields, which generates repulsion to ‘push’ users away from obstacles and other users, and uses gravity to ‘attract’ users to an open or unobstructed space. In this method, the users not only get repulsive forces from walls, but also from other users and their future states that are called avatars. At the same time, the users will get gravitational force from the steering target. The target selection considers the size of open space, the distance between the steering target and the boundary of physical space, and the distance between the steering target and the center of the physical space. Therefore, the system can steer users to an open area in the physical space to further reduce collisions. To verify the validity of our method, we developed a software to statistically analyze the influence of different factors, such as the physical space size and the number of users. Data from experiments shows that our method reduces the potential user resets by about 20%.
Selection of metrics in software quality evaluation

Abstract An expert method is proposed, both for assessment and for ensuring the software quality with a lack of information. Quality metrics (factors, indicators) are offered, which are most often used in assessing the software quality. Modified method for assessing the software quality is considered, which allows both improving the quality of software and evaluating the material costs necessary to improve the quality of software. This method can be applied to assessing the quality of various things, in particular, to assess the software quality, educational, medical and other services.