Reviewerly/exHarmony huggingface dataset:
Multi View Video Summarization Using RNN and SURF Based High Level Moving Object Feature Frames

Multi-View Video summarization is a process to ease the storage consumption that facilitates organized storage, and perform other mainline videos analytical task. This in-turn helps quick search or browse and retrieve the video data with minimum time and without losing crucial data. In static video summarization, there is less challenge in time and sequence issues to rearrange the video-synopsis. The low-level features are easy to compute and retrieve. But for high-level features like event detection, emotion detection, object recognition, face detection, gesture detection, and others requires the comprehension of the video content. This research is to propose an approach to over- come the difficulties in handling the high-level features. The distinguishable contents from the videos are identified by object detection and feature-based area strategy. The major aspect of the proposed solution is to retrieve the attributes of a motion source from a video frame. By dividing the details of the object that are available in the video frame wavelet decomposition are achieved. The motion frequency scoring method records the time of motions in the video. The frequency motion feature of video usage is a challenge given the continuous change of objects shape. Therefore, the object position and corner points are spotted using Speeded Up Robust Features (SURF) feature points. Support vector machine clustering extracts keyframes. The memory-based re- current neural network (RNN) recognizes the object in the video frame and remembers a long sequence. RNN is an artificial neural network where nodes form a temporal relationship. The attention layer in the proposed RNN network extracts the details about the objects in motion. The motion objects identified using the three video clippings is finally summarized using video summarization algorithm. To perform the simulation, MATLAB R 2014b software was used.
Difference-in-level Detection from RGB-D Images

Most robots implicitly assume that the road surface on which they move is flat, without differences in level. Detecting differences in level on roads contributes to robots moving safely without stacking and falling. Although there are some studies on detecting differences in level in RGB or RGB-D images, directly finding only differences in level on roads is difficult due to the abundance and complexity of the types of differences in level on roads. This paper presents a new method for detecting differences in level from RGB-D images obtained by a modern smartphone equipped with a high-performance depth camera. First, we extract a part of differences in level on roads by finding the change of the normal vector in the contour of the detected plane. Then, a deep learning model trained on the dataset created by using the extracted image patches is used for detecting all the differences in level in outdoor images. To evaluate the effectiveness of the proposed method, quantitative and qualitative comparisons with existing methods were conducted. Further, the results from various inputs were qualitatively and quantitatively evaluated. As a result, we verified that the proposed method was able to detect all differences in level in an image, even in complex scenes where existing methods cannot detect.
MDANet: Unsupervised, Mixed-Domain Adaptation for Semantic Segmentation of Remote Sensing Images

The imaging process of optical remote sensing images are easily affected by external conditions. Therefore, remote sensing images under different imaging conditions often show color differences, resulting in feature distribution differences between the source and target domain, hindering the migration of semantic segmentation models between domains. Currently, most domain adaptation methods are for single-source and single-target domains. Here, we proposed a novel and concise method, coined MDANet, for the adaptation of patch images of multi-source and multi-target domains and for reducing the distribution differences of different patch images by projecting them onto the virtual center of a mixed-domain. MDANet is a lightweight and self-supervised network that can be grafted with any semantic segmentation model. Our method significantly improved the segmentation accuracy of semantic segmentation models and showed higher stability and competitiveness than existing methods.
The Use of Digital Media as a Strategy for Lowering Anxiety in Learning English as a Foreign Language

In the modern era, the use of English now being spreading and penetrated to many fields, including educational field. Thus, learning a foreign language, particularly English is needed by students or learners. However, there are personality factors such as anxiety, that may give either good or bad influences. Therefore, strategy in learning is needed to help the learners encountering their learning process. Various strategies can be used in learning a foreign language. This study discusses about the role of digital media in helping lowering learners’ anxiety in learning foreign language especially English. Several studies are found and have conducted their analysis concerning the use of digital media such as social media, video games, video and audio, in foreign language learning. The results of the review show that digital media which offers low pressure to its users is advantageous and can be effectively used to help the learners in comprehending English so that they feel less anxiety and be more confident in learning and using English as a foreign language.
Blockchain in Social Media: Eliminating Centralized Control vs. Challenges

Blockchain has expanded beyond the concept of digital currency becoming essential to existing and emerging markets and digital practices. Today, Blockchain technology represents the most well-known decentralized technique, that has been considered in developing the new generation of decentralized social media platforms. This paper focuses on the alternatives of the centralized and profit-driven platforms: open-source and Decentralized Online Social Networks (DOSNs). Firstly, we give an overview of our topic of concern, explaining the main concepts and trends in the field. Then, we shed light on the opportunities and challenges of Blockchain in social media by emphasizing the main features and outcomes. We conclude that Blockchain encourages the exchange of reliable data and information on social media. It is a potential infrastructure that ensures better privacy and a prospective approach to deliver more actions with smart contracts and applications.
Top Management Support, Knowledge of Accounting Employee, Use of Information Technology on The Quality of Accounting Information System (AIS)

Purpose: This study aim for testing the effect of Top Management Support, Knowledge of Accounting Employee, Use of Information Technology on the Quality of Accounting Information System (AIS).&#x0D; Design/ methodology / approach: This research is an associative quantitative research. The data used in this research is secondary data; previous research book and journals references and primary data; questionnaire spreaded to respondents. Technique of collecting data in this study was using the survey method with 145 respondents who were tested with the PLS (Partial Least Square) application.&#x0D; Findings: The results of this study indicate that Top Management Support and the use of information technology have a positive and significant effect on the quality of Accounting Information System, while the knowledge of Accounting Employee has a positive but not significant effect on the quality of Accounting Information System.&#x0D; Research limitations/implications: The questionnaires was distributed only to financial institutions. However, the distribution of this questionnaire was only limited to the recipients of the letter ( receptionist ) from the financial institutions due to the Covid-19 pandemic and it is possible that the respondents did not understand the contents of the questionnaire given the lack of explanation and understanding provided by the researcher. In addition, limited number of samples and didn’t fulfill the representation of all regions in Indonesia.&#x0D; Originality/value: The Paper is Original&#x0D; Paper Type: Research paper
OPTIMAL ASSIGNMENT STRATEGY FOR DYNAMIC WORKFLOW OF REMOTE SENSING BIG DATA PROCESSING

The advent of the era of Remote Sensing Big Data has produced a large number of processing and analysis tasks, which require powerful computing capabilities to support. The computational efficiency of distributed computer clusters which are the most commonly used parallel computing architecture for high performance computing can be significantly improved through an effective task scheduling strategy. In this paper, in order to improve data computing efficiency, we propose a dynamic load balancing strategy for remote sensing data processing workflow tasks based on the Hungarian algorithm for heterogeneous distributed computing clusters. We also compare this strategy with the classic load balancing algorithm. We find that the speed-up effect of the strategy proposed in this paper is better, and the speedups become more pronounced as the number of tasks increases.
Auto Labeling to Increase Aspect-Based Sentiment Analysis Using K-Nearest Neighbors Method

Social media platforms generate many opinions, emotions, and views on all public services. Sentiment analysis is used in various institutions, such as universities, the business industry, and politicians. The evaluation process requires some data, both quantitative and qualitative. Researchers only focus on quantitative data but ignore qualitative data. The evaluation process given by students in the form of a review is qualitative data that is not structured, so it cannot use conventional methods. Unstructured data requires analysis as well as labeling. The labeling process of large amounts of data is a waste of time and money. Data labeling requires very high accuracy to avoid errors. Accuracy in data labeling is used for the process of classifying, training, and testing data. This study aims to automate data labeling using the K-Nearest Neighbors algorithm method. This labeling process can improve the accuracy of sentiment analysis. The results of the classification method can classify responses from Twitter users and can be used by universities as material for evaluating and assessing higher education services. The results of using a confusion matrix with 1.409 data obtained an accuracy rate of 79.43% with a value of k=15
Implementation of Voiceprint Recognition Using SVM and SimpleVGGNet Method based on MFCC

Voiceprint Recognition is a practical Recognition Technique that is a part of Biometric Technology. In recent years, both Machine Learning and Deep Learning have produced remarkable results in Pattern Classification, giving Voiceprint Recognition Technology a fresh strategy. As a result, utilizing the Support Vector Machine (SVM) in Machine Learning and Deep Learning framework of SimpleVGGNet, this study retrieved the MFCC characteristics of voiceprint and performed Voiceprint Recognition. We conducted experiments on two different Datasets respectively. These trials show that the Accuracy obtained by SimpleVGGNet is generally better than that of SVM, and SimpleVGGNet performed better in a large dataset, which can reach the Accuracy of 0.88. In the process of using different combinations of data as training data, we found that the closeness and gender of individuals within the data can also impact the quality of the model, with high intimacy between people reducing the accuracy of recognition, and the difference in vocal patterns between girls appearing to be smaller than the distinction between men.
Named Entity Recognition in Chinese E-commerce Domain Based on Multi-Head Attention

At present, the demand for entity recognition for product query and product recommendation systems of e-commerce domain is growing. Traditional methods that rely on experts to define artificial features and domain knowledge can no longer meet the needs of the domain due to their low recognition accuracy. According to the above background, a Chinese named entity recognition method in the e-commerce domain that incorporates a multi-headed attention mechanism and bidirectional long-short term memory network is proposed. A word vector represents the product title statements of e-commerce platforms. The generated word vector sequences are fed into a bi-directional long-short term memory network that mines their contextual semantic features. A multi-headed attention mechanism is introduced to focus on the entity words in the text to unearth their hidden features. In contrast, the entity recognition results are labeled by calculating the joint probability of sequence labels through conditional random fields. The experimental data show that the method can reach 86.16% accuracy and 86.57% F1 value for entity recognition in the e-commerce domain, which is practical and feasible.
Point Cloud Color Constancy

In this paper, we present Point Cloud Color Constancy, in short PCCC, an illumination chromaticity estimation algorithm exploiting a point cloud. We leverage the depth information captured by the time-of-flight (ToF) sensor mounted rigidly with the RGB sensor, and form a 6D cloud where each point contains the coordinates and RGB intensities, noted as (x,y,z, r,g, b). PCCC applies the PointNet architecture to the color constancy problem, deriving the illumination vector point-wise and then making a global decision about the global illumination chromaticity. On two popular RGB-D datasets, which we extend with illumination information, as well as on a novel benchmark, PCCC obtains lower error than the state-of-the-art algorithms. Our method is simple andfast, requiring merely 16 x 16-size input and reaching speed over 140 fps (CPU time), including the cost of building the point cloud and net inference.
Forecasting of Maximum Temperature by using ANFIS and GRU Algorithms: Case Study in Jakarta, Indonesia

Climate change is a highly complex phenomenon and has a significant impact on daily life. One of the climate features is temperature. When the temperature is high, the electricity requested by the population is more. That is due to the sophisticated technologies that the people need to employ to decrease the hotness. In this study, two methods from the Deep Learning discipline, namely Adaptive Neuro-Fuzzy Inference System (ANFIS)and Gated Recurrent Unit (GRU), are used to forecast Maximum Temperature (MT) time series. This research use dataset from ECWMF ERA-5. The objective is to analyze and compare GRU and ANFIS methods to experimental results and provide more insights into their capabilities to forecast the Maximum Temperature. Based on the result, both ANFIS and GRU can target the forecasting of MT. The two models give satisfactory performance because the CC values are above 0.95, and both models' RMSE and MAPE are lower than 2. Moreover, the GRU algorithm is more efficient for short-term forecasting. Besides, the ANFIS model improves its effectiveness when the forecasting is longer. For the training time, the GRU algorithm gives far shorter computational time than the ANFIS.
Face Recognition in Machine Learning: A Framework for Dimensionality Reduction Algorithms

A facial recognition system matches a human face from a digital image or a video frame against an authentic repository of faces or Eigenfaces subject to algorithmic performance and detection accuracy. Dimensionality reduction is a type of unsupervised learning for which input is images of higher-dimensional data and these images are represented with a lower-dimensional space. The purpose of the research paper is to evaluate the performance of Dimensionality Reduction algorithms for face recognition using different approaches of Machine Learning (ML). The research uses the Interpretivist Paradigm characterised by a subjectivist epistemology, relativist ontology, naturalist methodology, and a balanced axiology. The quantitative methodology with an experimental research design was used. The results of the experiment show that only selecting the top M eigenfaces reduces the dimensionality of the data, and that too few eigenfaces results in too much information loss, and hence less discrimination between faces. With increasing dimensionality, the amount of training instances needed rises exponentially (i.e., kd). The performance of the Dimensionality Reduction Algorithm is benchmarked against the Clustering, Bayesian, Genetic, Reinforcement Q-Learning and Reinforcement A3C Algorithms. The outcome of the research makes significant value-adding contributions to the future of advances in Big Data Analytics and ML.
Optimization of the C4.5 Algorithm by Using a Genetic Algorithm for the Diagnosis of Life Expectancy for Hepatitis Patients

As technology develops rapidly, the amount of data generated experiencing rapid development, including medical data. Data can help diagnose the life expectancy of people with the disease such as hepatitis using data mining methods in the medical field. In this research, technique data mining uses a classification technique with the C4.5 algorithm and the UCI Machine Learning Repository dataset. This dataset has 19 attributes, 1 class, and 155 samples. C4.5 algorithm is optimized using the Genetic Algorithm feature selection process. This study compares the accuracy of the C4.5 algorithm before and after optimization using a Genetic Algorithm. C4.5 algorithm produces the highest accuracy of 96.23%. Meanwhile, the C4.5 algorithm, after being optimized using Genetic Algorithm, has the highest accuracy of 98.11%. The number of features selected is 15 features. Application of Genetic Algorithms in C4.5 algorithm is proven to improve the accuracy in diagnosing life expectancy of people with hepatitis as much as 1.88%.
Adaptive IoT Enabled Flood Management System: An Artificial Intelligence Approach

Flood control is one of the most important areas where practical measures have been implemented to minimise damage.We are developing an IoT-enabled adaptive AI technique for efficient fluid management in this research project.In recent years, floods in Kerala, India, have caused significant damage to people, infrastructure, and the environment.Precipitation rates, temperature, and other data were collected by various IoT devices.Data on floods in Kerala is collected and sorted into two categories: 80% for training and 20% for testing.Six different machine learning models are trained, and the proposed hybrid model achieves a maximum accuracy of 99.64%.We were able to avoid losses as a result of integration.
Experimental and Computational Study on the Ground Forces CGF Automation of Wargame Models Using Reinforcement Learning

Wargame is an important tool that enables training units to develop various strategies by allowing them to experience unexpected situations. There are three methodologies that determine the behavior of the Computer Generated Forces(CGF) in wargame—rule-based, agent-based, and learning-based methodologies. The military determines the behaviors of the CGF mainly based on the rules because a doctrine and an operation plan are well established. However, the advent of intelligent weapons and the accompanying changes in tactics will make it difficult to expect an environment and situations of the future battlefield. Therefore, we studied the automation of CGF through reinforcement learning in order to give unexpected situations, so that the training unit would be able to establish various strategies and tactics through the wargame model. Based on the combat functions of the ground forces, we configured multiple environments that the ground forces CGFs will learn in. First, infantry and artillery CGFs learned in the close combat environment, which is the basis of ground forces combat. Second, the trainee CGF learned in the context of military training. Third, the drone CGF learned how to reconnaissance and attack in a multi-drone environment, and finally, the combat service support CGF learned under the mission of supplying ammunition. As a result, we confirmed that the reinforcement learning methodology is applicable to CGF through these experiments.
FADE: Fusing the Assets of Decoder and Encoder for Task-Agnostic Upsampling

We consider the problem of task-agnostic feature upsampling in dense prediction where an upsampling operator is required to facilitate both region-sensitive tasks like semantic segmentation and detail-sensitive tasks such as image matting. Existing upsampling operators often can work well in either type of the tasks, but not both. In this work, we present FADE, a novel, plug-and-play, and task-agnostic upsampling operator. FADE benefits from three design choices: i) considering encoder and decoder features jointly in upsampling kernel generation; ii) an efficient semi-shift convolutional operator that enables granular control over how each feature point contributes to upsampling kernels; iii) a decoder-dependent gating mechanism for enhanced detail delineation. We first study the upsampling properties of FADE on toy data and then evaluate it on large-scale semantic segmentation and image matting. In particular, FADE reveals its effectiveness and task-agnostic characteristic by consistently outperforming recent dynamic upsampling operators in different tasks. It also generalizes well across convolutional and transformer architectures with little computational overhead. Our work additionally provides thoughtful insights on what makes for task-agnostic upsampling. Code is available at: http://lnkiy.in/fade_in .
Effects of prosodic structure on the temporal organization of speech and co-speech gestures

Although many studies have observed a close relationship between prosodic structure and co-speech gestures, little is understood about cross-modal gestural coordination. The present study examines the relationship between articulatory and co-speech gestures at prosodic boundaries and under prominence, focusing on non-referential manual and eyebrow beat gestures in Korean, a language in which co-speech gestures are virtually unexplored. This study hypothesizes that prosodic structure systematically governs the production of both speech and co-speech gestures and their temporal organization. Multimodal signals of a story reading were collected from eight speakers (5F, 3M). The lips, tongue, and eyebrows were point-tracked using EMA, and the vertical manual movements obtained from a video recording were auto-tracked using a geometrical centroid tracking method. Measurements taken included the duration of intervals from the timepoint of concurrent beat gesture onset and target to 1) consonant gesture onset and target, 2) vowel gesture onset and target, 3) pitch gesture (F0) onset and target, and 4) phrasal boundaries. Results reveal systematic inter-articulator coordination patterns, suggesting that beat gestures co-occurring with speech gestures are recruited to signal information grouping and highlighting. The findings are discussed with reference to the nature of prosodic representation and models of speech planning. [Work supported by NSF.]
TTS - VLSP 2021: The Thunder Text-To-Speech System

This paper describes our speech synthesis system participating in the Vietnamese Text-To-Speech track of the 2021 VLSP evaluation campaign. The goal of this challenge is to build a synthetic voice from a provided spontaneous speech corpus in Vietnamese. In this paper, we propose our implementation of FastSpeech2 model on spontaneous speech. We used a special strategy with spontaneous datasets using the TTS system. We present our utilization in generating mel-spectrograms from given texts and then synthesize speech from generated mel-spectrograms using a separately trained vocoder. In evaluation, our team achieved 3.943 mean score in MOS in-domain test, 3.3 in MOS out-domain test, and 85.00% SUS, which indicates the effectiveness of the proposed system.