Reviewerly/exHarmony huggingface dataset:
Boundary-aware pyramid attention network for detecting salient objects in RGB-D images

Recent developments in convolutional neural networks (CNNs) have significantly improved the results of salient object detection (SOD), particularly RGB-D SOD. This article proposes BPA-Net (Boundary-aware Pyramid Attention Network), a network that addresses two key issues in RGB-D SOD based on CNNs: 1) accurately locking the position of an object when it is unclear whether it is a multi-object or a single object, and 2) depicting fine edges and fill pixels while maintaining robustness with complex scenes and similarly-colored backgrounds. Accordingly, we model three network branches to solve these problems separately. To address the first problem, we devise the Multi-scale Attention Branch, a pyramid attention network that collects the positions of objects, thereby eliminating interference from non-objects. The second is addressed via a Boundary Refine Branch that uses a depth image to capture the edges of objects. This step refines the boundaries of objects and emphasizes the importance of salient edge information. Such branches are learned for obtaining precise salient boundaries and for position estimation and are subsequently combined with a coarse salient map generated by the Coarse Salient Detection Branch, an encode-decode SOD network, to improve salient object segmentation. Extensive experiments show that our BPA-Net outperforms state-of-the-art approaches on two popular benchmarks.
Evaluating models of robust word recognition with serial reproduction

Spoken communication occurs in a "noisy channel" characterized by high levels of environmental noise, variability within and between speakers, and lexical and syntactic ambiguity. Given these properties of the received linguistic input, robust spoken word recognition -- and language processing more generally -- relies heavily on listeners' prior knowledge to evaluate whether candidate interpretations of that input are more or less likely. Here we compare several broad-coverage probabilistic generative language models in their ability to capture human linguistic expectations. Serial reproduction, an experimental paradigm where spoken utterances are reproduced by successive participants similar to the children's game of "Telephone," is used to elicit a sample that reflects the linguistic expectations of English-speaking adults. When we evaluate a suite of probabilistic generative language models against the yielded chains of utterances, we find that those models that make use of abstract representations of preceding linguistic context (i.e., phrase structure) best predict the changes made by people in the course of serial reproduction. A logistic regression model predicting which words in an utterance are most likely to be lost or changed in the course of spoken transmission corroborates this result. We interpret these findings in light of research highlighting the interaction of memory-based constraints and representations in language processing.
Robot Eye: Automatic Object Detection And Recognition Using Deep Attention Network to Assist Blind People

Detection and Recognition is a well-known topic in computer vision that still faces many unresolved issues. One of the main contributions of this research is a method to guide blind people around an outdoor environment with the assistance of a ZED stereo camera, a camera that can calculate depth information. In this paper, we propose a deep attention network to automatically detect and recognize objects. The objects are not only limited to general people or cars, but include convenience stores and traffic lights as well, in order to help blind people cross a road and make purchases in a store. Since public datasets are limited, we also create a novel dataset with images captured by the ZED stereo camera and collected from Google Street View. When testing with images of different resolutions, our method achieves an accuracy rate of about 81%, which is better than naive YOLO v3.
What does good look like?

This short review discusses the outcomes of the 3 rd Workshop of the International and Interdisciplinary Perspectives on Children &amp; Recommender and Information Retrieval Systems (KidRec 2019) , co-located with the 2019 ACM Interaction Design and Children (IDC) Conference, which took place June 12-15 in Boise, Idaho, USA. The goal for the workshop was to explore the characteristics of a "good" information retrieval system for children. The diversity of attendees - including industry representatives, undergraduate and graduate students, and senior scholars in various areas of computer science - made it possible to continue to build community around this important topic and further discuss and outline the salient concerns and the next steps to promote further exploration in this area.
Modeling Autoregressive Integrated Moving Average (ARIMA) and Forecasting of PT Unilever Indonesia Tbk Share Prices During the COVID-19 Pandemic Period

Abstract ARIMA method is one method that can be used in predicting the movement of company shares. This study aims to obtain a time series model with the ARIMA method and predict stock price data of PT Unilever Indonesia Tbk from January 2020 to June 2020. The best model that fits the data based on the MSE value is the ARIMA(1,1,1) model. The ARIMA model (1,1,1) shows a match between real data and the predicted value. This model is then used for forecasting the next 14 days. Data on UNVR stock price from January 2020 to June 2020 are below 8000, this seems to correlate with the current conditions, namely the Covid-19 pandemic. Forecasting for the next 14 days (two weeks) from July 1, 2020 to July 14, 2020, the forecast values have a trend decrease, the trend of PT. Unilever Indonesia Tbk has been going down since January 2020. This seems to have occurred as an implication of the Covid-19 pandemic from January 2020 to the present.
LiDAR - Stereo Camera Fusion for Accurate Depth Estimation

Dense 3D reconstruction of the surrounding environment is one the fundamental way of perception for Advanced Driver-Assistance Systems (ADAS). In this field, accurate 3D modeling finds applications in many areas like obstacle detection, object tracking, and remote driving. This task can be performed with different sensors like cameras, LiDARs, and radars. Each one presents some advantages and disadvantages based on the precision of the depth, the sensor cost, and the accuracy in adverse weather conditions. For this reason, many researchers have explored the fusion of multiple sources to overcome each sensor limit and provide an accurate representation of the vehicle's surroundings. This paper proposes a novel post-processing method for accurate depth estimation, based on a patch-wise depth correction approach, to fuse data from LiDAR and stereo camera. This solution allows for accurate edges and object boundaries preservation in multiple challenging scenarios.
EVALUATION OF SOLAR RADIATION ESTIMATED FROM HIMAWARI-8 SATELLITE OVER VIETNAM REGION

The development of Solar energy system is growing rapidly in Vietnam in recent years by encouragement of the Government in renewable energy. Requirement for accurate knowledge of the solar radiation reaching the surface is increasingly important in the successful deployment of Solar photovoltaic plants. However, measurements of different components of solar resources including direct normal irradiance (DNI) and global horizontal irradiance (GHI) are limited to few stations over whole country. Satellite imagery provides an ability to monitor the surface radiation over large areas at high spatial and temporal resolution as alternatives at low cost. Observations from the new Japanese geostationary satellite Himawari-8 produce imagery covering Asia-Pacific region, permitting estimation of GHI and DNI over Vietnam at 10-minute temporal resolution. However, accurate comparisons with ground observations are essential to assess their uncertainty. In this study, we evaluated the Himawari-8 radiation product AMATERASS provided by JST/CREST TEEDDA using observations recorded at 5 stations in different regions of Vietnam. The result shows good agreement between satellite estimation and observed data with high correlation of range 0.92-0.94, but better in clear-sky episodes.Because of AMATERASS outperform, we used it for validating ERA-Interim reanalysis in the spatial scale. The comparison was made dividedly for 7 climate zones and 4 seasons. The conclusion is that ERA-Interim is also well associated with satellite-based estimates in seasonal trend for all season, but in average the reanalysis has negative bias towards satellite estimates. This underestimation is more pronounced in the months of JJA and SON periods and in the north part of Vietnam because of unpredicted cloud in the ERA reanalysis.
Underwater magnetic target signal denoising based on modified wavelet decomposition and reconstruction algorithm

Abstract Noise reduction is crucial for magnetic anomaly signal detection of underwater ferromagnetic target. Modified wavelet decomposition and reconstruction algorithm is proposed to suppress the colored noise and improve the signal to noise ratio. Hamming window is employed to make the preprocessed signal continuous. Evaluation index based on signal-to-noise ratio function is selected, wavelet decomposition and reconstruction algorithm iterates adaptively to select the optimal order of decomposition and reconstruction. The experiment result emphasizes that the signal-to-noise ratio of novel algorithm is 71.6dB. In this particle, we provide a new method to improve signal-to-noise ratio and enhance real-time signal processing for underwater target magnetic anomaly signal detection.
A Hybrid Compression Method for Medical Images Based on Region of Interest Using Artificial Neural Networks

The number of medical images being stored and communicated daily is rapidly increasing, according to the need for these images in medical diagnoses. Hence, the storage space and bandwidths required to store and communicate these images are exponentially increasing, which has brought attention toward compressing these images. In this study, a new compression method is proposed for medical images based on convolutional neural networks. The proposed neural network consists of two main stages: a segmentation stage and an autoencoder. The segmentation stage is used to recognize the Region of Interest (ROI) in the image and provide it to the autoencoder stage, so more emphasis on the information of the ROI is applied. The autoencoder part of the neural network contains a bottleneck layer that has one-eighth of the dimensions of the input image. The values in this layer are used to represent the image, while the following layers are used to decompress the images, after training the neural network. The proposed method is evaluated using the CLEF MED 2009 dataset, where the evaluation results show that the method has significantly better performance, compared to the existing state-of-the-art methods, by providing more visually similar images using less data.
Open-source toolkit for end-to-end Korean speech recognition

<h2>Abstract</h2> A modular and extensible end-to-end Korean automatic speech recognition (ASR) toolkit based on the deep learning library PyTorch called as <i>KoSpeech</i><sup>1</sup>1This paper is written based on our technical report available at: https://arxiv.org/abs/2009.03092. was released as an opensource software. Several ASR open-source toolkits have been released, but all of them deal with non-Korean languages. For this reason, The purpose of <i>KoSpeech</i> is to provide a customizable training environment for Korean ASR researchers. Furthermore, <i>KoSpeech</i> provides more than fifty options. Researchers can conveniently customize various hyperparameters. Because of these advantages, <i>KoSpeech</i> can be a very useful toolkit for Korean ASR researchers and could be a guideline for those who research Korean speech recognition.
A VR/AR-based Display System for Arts and Crafts Museum

Traditionally, the museum statically displays its art collections by means of windows. Such approach is limited in certain aspects, such as insufficient information expression, poor user interaction, and limited space. By leveraging Virtual Reality (VR) technology, this paper proposes a new museum exhibition system to address the above limitations. Our design is demonstrated in the exhibition system at China Arts and Crafts Master Museum of Nanchang Institute of Science & Technology. Experiments show that the VR-based system can display the information of the museum's collections more comprehensively and more effectively. Visitors can then better understand the artistic essence of museum collections, inspire visitors' observation and thinking, and consequently achieve a deeper level of interaction experience.
The cyber security in the age of telework: A descriptive research framework through science mapping

Teleworking is a work model that has been widely accepted by organizations, mostly since it has many advantages over traditional work. Most organizations have implemented it to attract new human talent and keep the existing one, preventing the flight of intellect or theft by competition. For the worker it also presents several advantages, such as savings in travel times, savings in fuel costs and more free time. However, one of the many disadvantages of teleworking is information security. We live in a digitized world, where cyber attacks represent a great threat for users and organizations, attacks that are increasing day by day. Cyber security, also known as information technology security or electronic information security, has become a necessity in the world we live in, where ICTs have had a great development as well as acceptance by users. Cyber security seeks to identify the strategies, to implement and to maintain a safe environment, but also to prevent threats and / or attacks. The main objective of cyber security is to generate trust between customers, suppliers and the general market, about the information that is handled on the network. Cyber security must be one step ahead of crime to offer support in all the digital movements that we do.
The Study of Multiple Classes Boosting Classification Method Based on Local Similarity

Boosting of the ensemble learning model has made great progress, but most of the methods are Boosting the single mode. For this reason, based on the simple multiclass enhancement framework that uses local similarity as a weak learner, it is extended to multimodal multiclass enhancement Boosting. First, based on the local similarity as a weak learner, the loss function is used to find the basic loss, and the logarithmic data points are binarized. Then, we find the optimal local similarity and find the corresponding loss. Compared with the basic loss, the smaller one is the best so far. Second, the local similarity of the two points is calculated, and then the loss is calculated by the local similarity of the two points. Finally, the text and image are retrieved from each other, and the correct rate of text and image retrieval is obtained, respectively. The experimental results show that the multimodal multi-class enhancement framework with local similarity as the weak learner is evaluated on the standard data set and compared with other most advanced methods, showing the experience proficiency of this method.
Comparison between Convolutional Neural Network and Convolutional Neural Network-Support Vector Machines as the classifier for Colon Cancer

Colon Cancer begins in the rectum, and it grows in the last part of the large intestine. In the early stages, there are no symptoms, and it can be identified by the machine learning method. Convolutional Neural Network is a popular method used in machine learning in a wide range of application domains that is known for its high accuracy value. In addition, there is a Support Vector Machine method with several kernel functions that has been applied in the classification. Therefore, the research is aimed at the performance and accuracy of Convolutional Neural Network, and Convolutional Neural Network-Support Vector Machine as the classification of colon cancer.
Can You All Look Here? Towards Determining Gaze Uniformity In Group Images

Since the advent of the smartphone, the number of group images taken every day is rising exponentially. The photographers' struggle is to make sure everyone looks at the camera while taking the picture. More specifically, in a group image, if everybody is not looking in the same direction, then the image's aesthetic quality and utility are depreciated. The photographer usually discards the image, and then subsequently, several images are taken to mitigate this issue. Usually, users have to manually check if the image is uniformly gazed, which is tedious and time-consuming. This paper proposes a method for classifying a given group image as uniformly gazed or nonuniformly gazed by calculating the Gaze Uniformity Index. We evaluate the proposed method on a subset of the `Images of Groups' dataset. The proposed method achieved an accuracy of 67%.
Random Forest vs. SVM vs. KNN in classifying Smartphone and Smartwatch sensor data using CRISP-DM

Nowadays IoT sensors become one of the major sources of data, starting from home automation going to daily devices. Focusing on handsets that have the most significant contribution factor on data blooming and booming. The problem at hand is data mining methods comparison, for selecting the best method that serves sensor data. Methodology used was CRISP-DM. This paper found Random Forest data mining method to work best under the described circumstances.
Deep Precipitation Downscaling

Precipitation downscaling, which is similar to the mechanism of single-image super-resolution (SR), aims to improve the spatial resolution of rain maps. It is of great practical value and theoretical significance. This letter presents a new deep precipitation downscaling (DPD) method, named auxiliary guided spatial distortion (AGSD) network, motivated by SR techniques. Specifically, an auxiliary guided module (AGM), which takes multiple meteorological elements (e.g., temperature, relative humidity, and wind) as input, is proposed for getting more accurate rain map features. Meanwhile, a simple but effective spatial distortion module (SDM) is proposed. Benefitting from SDM, the DPD model can rectify the rain map via terrain correlation. Furthermore, to improve the model performance among various rain intensity (including small rain, moderate rain, heavy rain, and storm), a threat score-driven pseudo threat-score (PTS) loss is presented. Experimental results compared with state-of-the-art methods demonstrate the superiority of the proposed method.
Cybersecurity in Medical Private Practice: Results of a Survey in Audiology

The following topics are dealt with: security of data; Internet of Things; learning (artificial intelligence); cloud computing; medical information systems; Internet; graph theory; health care; data privacy; resource allocation.
Transferring model structure in Bayesian transfer learning for Gaussian process regression

Bayesian transfer learning (BTL) is defined in this paper as the task of conditioning a target probability distribution on a transferred source distribution. The target globally models the interaction between the source and target, and conditions on a probabilistic data predictor made available by an independent local source modeller. Fully probabilistic design is adopted to solve this optimal decision-making problem in the target. By successfully transferring higher moments of the source, the target can reject unreliable source knowledge (i.e. it achieves robust transfer). This dual-modeller framework means that the source’s local processing of raw data into a transferred predictive distribution – with compressive possibilities – is enriched by (the possible expertise of) the local source model. In addition, the introduction of the global target modeller allows correlation between the source and target tasks – if known to the target – to be accounted for. Important consequences emerge. Firstly, the new scheme attains the performance of fully modelled (i.e. conventional) multitask learning schemes in (those rare) cases where target model misspecification is avoided. Secondly, and more importantly, the new dual-modeller framework is robust to the model misspecification that undermines conventional multitask learning. We thoroughly explore these issues in the key context of interacting Gaussian process regression tasks. Experimental evidence from both synthetic and real data settings validates our technical findings: that the proposed BTL framework enjoys robustness in transfer while also being robust to model misspecification.
Cloud Security Implementation using Homomorphic Encryption

With the advancement of computing and communication technologies, data transmission in the internet are getting bigger and faster. However, it is necessary to secure the data to prevent fraud and criminal over the internet. Furthermore, most of the data related to statistics requires to be analyzed securely such as weather data, health data, financial and other services. This paper presents an implementation of cloud security using homomorphic encryption for data analytic in the cloud. We apply the homomorphic encryption that allows the data to be processed without being decrypted. Experimental results show that, for the polynomial degree 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> , 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> , and 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sup> , the total executions are 2.2 ms, 4.4 ms, 25 ms per data, respectively. The implementation is useful for big data security such as for environment, financial and hospital data analytics.
Towards robust production machine learning systems

The advances in machine learning (ML) have stimulated the integration of their capabilities into software systems. However, there is a tangible gap between software engineering and machine learning practices, that is delaying the progress of intelligent services development. Software organisations are devoting effort to adjust the software engineering processes and practices to facilitate the integration of machine learning models. Machine learning researchers as well are focusing on improving the interpretability of machine learning models to support overall system robustness. Our research focuses on bridging this gap through a methodology that evaluates the robustness of machine learning-enabled software engineering systems. In particular, this methodology will automate the evaluation of the robustness properties of software systems against dataset shift problems in ML. It will also feature a notification mechanism that facilitates the debugging of ML components.