Reviewerly/exHarmony huggingface dataset:
ESSN: Enhanced Semantic Segmentation Network by Residual Concatenation of Feature Maps

Semantic segmentation performs pixel-level classification of multiple classes in the input image. Previous studies on semantic segmentation have used various methods such as multi-scale image, encoder-decoder, attention, spatial pyramid pooling, conditional random field, and generative models. However, the contexts of various sizes and types in diverse environments make their performance limited in robustly detecting and classifying objects. To address this problem, we propose an enhanced semantic segmentation network (ESSN) robust to various objects, contexts, and environments. The ESSN can extract multi-scale information well by concatenating the residual feature maps with various receptive fields extracted from sequential convolution blocks, and it can improve the performance of semantic segmentation without additional modules such as loss or attention during the training process. We performed the experiments with two open databases, the Stanford background dataset (SBD) and Cambridge-driving labeled video database (CamVid). Experimental results demonstrated the pixel acc. of 92.74%, class acc. of 79.66%, and mIoU of 71.67% with CamVid, and pixel acc. of 87.46%, class acc. of 81.51%, and mIoU of 71.56% with SBD, which are higher than those of the existing state-of-the-art methods. In addition, the average processing time were 31.12 ms and 92.46 ms on the desktop computer and Jetson TX2 embedded system, respectively, which confirmed that ESSN is applicable to both the desktop computer and Jetson TX2 embedded system which is widely used in autonomous vehicles.
Scientometric Analysis and Visualization of Solar Physics Research in India

We analyzed scientific production of the solar physics research community over the past two decades in India. Scientometric techniques, especially the literature growth models, coauthorship, co-occurrence of keywords, citation, co-citation and bibliographic coupling are used for analysis by using the R program and VOS Viewer. Highly cited papers, prolific authors and journals are identified. The results of this study confess that the scientific literature on Solar Physics in India had grown exponentially with an annual growth rate of about 8.90% during the above mentioned period. Astrophysical Journal and Solar Physics were the most productive journals contributing 34.92% of the total publications. Sun: corona, Sun: magnetic fields and Sun: activity are the most frequently used keywords. United States and Germany are the most frequent international collaborating nations. About 91% of the published papers are multi-authored and only 9% of them are single authored. The mean relative growth rate and doubling time for India`s publication output is 0.19 and 4.9, respectively.
3PXNet

As the adoption of Neural Networks continues to proliferate different classes of applications and systems, edge devices have been left behind. Their strict energy and storage limitations make them unable to cope with the sizes of common network models. While many compression methods such as precision reduction and sparsity have been proposed to alleviate this, they don’t go quite far enough. To push size reduction to its absolute limits, we combine binarization with sparsity in Pruned-Permuted-Packed XNOR Networks (3PXNet), which can be efficiently implemented on even the smallest of embedded microcontrollers. 3PXNets can reduce model sizes by up to 38X and reduce runtime by up to 3X compared with already compact conventional binarized implementations with less than 3% accuracy reduction. We have created the first software implementation of sparse-binarized Neural Networks, released as open source library targeting edge devices. Our library is complete with training methodology and model generating scripts, making it easy and fast to deploy.
Face Recognition Using Raspberry PI

In an age where public security is a priority, there is a growing need for autonomous systems capable of monitoring hotspots to ensure public safety. Face recognition technology could create an increased level of security, enable businesses and governments to save money on CCTV monitoring staff, and increase business productivity by automating processes such as attendance monitoring. In recent decades, such a system would have been unfeasible to implement due to cost and technological restraints. This study aims to explore a real-time face recognition system using easily-attainable components and libraries, such as Raspberry PI and Dlib, Face Recognition library and Open Source Computer Vision Library (OpenCV). It also covers various face recognition machine learning algorithms. The results show that in real-time applications, the system runs at 2 frames per second and recognizes faces despite Raspberry PI's limitations such as low CPU and GPU processing power.
Automatic Essay Grading for Bahasa Indonesia with Support Vector Machine and Latent Semantic Analysis

This research is used to increase the accuracy of automatic short essay grading for Bahasa Indonesia. Short essay in Bahasa Indonesia are classified using Support Vector Machine (SVM) based on its topic to decrease unrelated answer then assessed using Latent Semantic Analysis. Term Frequency-Inverse Document Frequency (TF-IDF) is used to weigh word in short essay and the result will be an input on SVM. The output of SVM are assessed using Latent Semantic Analysis (LSA). Latent Semantic Analysis uses Term Frequency Matrix to represent text in matrix, Singular Value Decomposition to decompose these matrix, and Frobenius Norm to find the similarity of lectures' answer and students' answer In this research, parameter C value as 1 and kernel linear are used to obtain the highest accuracy of classification using Support Vector Machine, 97,297% with 50% portion of data as training and 50% portion of data as testing. The accuracy score obtained from LSA is 72,01%.
A comparative analysis of different load balancing algorithms on different parameters in cloud computing

In recent days Cloud computing has made the trademark in hosting and delivering services to the personal or any organization. Everywhere and everybody is opting for cloud computing because it's beneficial uses i.e. worth to adopt the infrastructure, platform and services on the model of pay for only those services which are going to be used. Cloud computing gives the best infrastructure, new softwares and extended applications without investing for infrastructure maintenance, downloading, and installation of the softwares and without paying for the license cost for the individual computers and no requirement of personnel training for extended applications. Cloud computing provides the way to increase the services and capability of any organization at run time. Still cloud computing is at its initial state and many more researches are going on in this field and there are many obstacles that prevents the success of cloud computing. In this paper, a survey of cloud computing basic concepts, characteristics of cloud computing, deployment models, Environment for Load Balancing algorithms as well as research challenges are included. The aim of this paper is to find out the research direction in this vast technology. We discussed performance parameters which must be satisfied to design the efficient and effective algorithm. The algorithm which makes the better trade-off with these parameters will be considered good.
Morphological Analysis of Speech Translation into Indonesian Sign Language System (SIBI) on Android Platform

The main problem that occurs with people with hearing impairment is the difficulty of communicating, both among fellow deaf and non-hearing impaired. This difficulty is caused by not all non-hearing impairments have skills in using sign language such as the Sistem Isyarat Bahasa Indonesia (SIBI). The presence of a SIBI dictionary cannot be used practically and less effective if people want to translate sentences to sign language. The goal that will be achieved in this research is to create assistive applications that can change the speech in form of sentences into SIBI so that it can facilitate communication between people with hearing impairment and people with non-hearing impairment. The specific target to be achieved in this research is the creation of an android application that can change the speech in form of sentences into SIBI using morphological analysis with modification of the Enhanced Confix Stripping (ECS) stemming algorithm. The method used consists of literature studies and requirement analysis, Android Speech API integration, parsing the sentence (through morphological analysis with modification of the ECS stemming algorithm), creating SIBI video datasets, testing algorithms, and testing overall. The results showed that the average accuracy of the Android Speech API is 94.06%, the average accuracy rate of morphological analysis with modification of the ECS stemming algorithm is 95%, and the overall level of conformity is 80,71%. These results indicate that overall the speech translator into SIBI is working very well.
Underwater sea cucumbers detection based on pruned SSD

We proposed method for sea cucumber detection based on SSD (Single Shot Multibox Detector) and convolution kernel pruning. Sea cucumbers images taken by underwater robot were enhanced by multi-scale Retinex firstly, which can make images clearer and enhance the details. A dataset was built based on data augmentation, there were 5530 labelled original images in the dataset totally and SSD was trained to detect the sea cucumbers. The redundant filters of the feature extraction network VGG16 were pruned based on convolution kernel pruning. The parameter quantity and calculation of the model were effectively reduced without loss of precision. The mean average precision of pruned SSD reached 89.54% on test dataset. The detection precision for underwater sea cucumbers was 94. 32% and the recall ratio was 91.09%. Compared with original model, the detection speed was increased by 20.52% to 46.4 frame/s. Compared with the detection performance of the pruned SSD in different object scale, different weather conditions, and other methods for object detection, the pruned SSD can reach a high level of both detection precision and speed, which also has better robustness. The pruned SSD proposed in this paper can realize the accurate and real time detection of sea cucumbers.
Merge-Swap Optimization Framework for Supervoxel Generation from Three-Dimensional Point Clouds

Surpervoxels are becoming increasingly popular in many point cloud processing applications. However, few methods have been devised specifically for generating compact supervoxels from unstructured three-dimensional (3D) point clouds. In this study, we aimed to generate high quality over-segmentation of point clouds. We propose a merge-swap optimization framework that solves any supervoxel generation problem formulated in energy minimization. In particular, we tailored an energy function that explicitly encourages regular and compact supervoxels with adaptive size control considering local geometric information of point clouds. We also provide two acceleration techniques to reduce the computational overhead. The performance of the proposed merge-swap optimization approach is superior to that of previous work in terms of thorough optimization, computational efficiency, and practical applicability to incorporating control of other properties of supervoxels. The experiments show that our approach produces supervoxels with better segmentation quality than two state-of-the-art methods on three public datasets.
Ensuring Computers Understand Manual Operations in Production: Deep-Learning-Based Action Recognition in Industrial Workflows

In this study, we consider fully automated action recognition based on deep learning in the industrial environment. In contrast to most existing methods, which rely on professional knowledge to construct complex hand-crafted features, or only use basic deep-learning methods, such as convolutional neural networks (CNNs), to extract information from images in the production process, we exploit a novel and effective method, which integrates multiple deep-learning networks including CNNs, spatial transformer networks (STNs), and graph convolutional networks (GCNs) to process video data in industrial workflows. The proposed method extracts both spatial and temporal information from video data. The spatial information is extracted by estimating the human pose of each frame, and the skeleton image of the human body in each frame is obtained. Furthermore, multi-frame skeleton images are processed by GCN to obtain temporal information, meaning the action recognition results are predicted automatically. By training on a large human action dataset, Kinetics, we apply the proposed method to the real-world industrial environment and achieve superior performance compared with the existing methods.
Image quality assessment based on deep learning with FPGA implementation

To improve image quality assessment (IQA) methods, it is believable that we have to extract image features that are highly representative to human visual perception. In this paper, we propose a novel IQA algorithm by leveraging an optimized convolutional neural network architecture that is designed to automatically extract discriminative image quality features. And the IQA algorithm uses local luminance coefficient normalization, dropout and the other advanced techniques to further improve the network learning ability. At the same time the proposed IQA algorithm is implemented based on Field Programmable Gate Array (FPGA) and further evaluated on two public databases. Extensive experimental results have shown that our method outperforms many existing IQA algorithms in terms of accuracy and speed.
Multi-under-Actuated Unmanned Surface Vessel Coordinated Path Tracking

Multi-under-actuated unmanned surface vehicles (USV) path tracking control is studied and decoupled by virtue of decentralized control. First, an improved integral line-of-sight guidance strategy is put forward and combined with feedback control to design the path tracking controller and realize the single USV path tracking in the horizontal plane. Second, graph theory is utilized to design the decentralized velocity coordination controller for USV formation, so that multiple USVs could consistently realize the specified formation to the position and velocity of the expected path. Third, cascade system theory and Lyapunov stability are used to respectively prove the uniform semi-global exponential stability of single USV path tracking control system and the global asymptotic stability and uniform local exponential stability of coordinated formation system. At last, simulation and field experiment are conducted to analyze and verify the advancement and effectiveness of the proposed algorithms in this paper.
Designing Intelligent Personalized Chatbot for Hotel Services

Development of chatbot in customer services and sales are remarkable. However, there is only little research in developing chatbot-hotel in Indonesia. Chatbot has many advantages for both hotel and mobile users to improve convenience, reduce service, sales and support costs, one-to-one marketing and new data collections. Using chatbot, customers using smartphone can reach a hotel anytime and anywhere with personalized services related to their needs. The customers can find out availability of rooms, price, booking and have a personal front office that provide the real-time and context-relevant information about hotel services. Therefore, we develop an interactive intelligent personalized chatbot-hotel by using AIML and Google Flutter. Flutter is used as it is easy to develop the chatbot-hotel in the cross-platform mobile application. The outcome of this work is a prototype chatbot-hotel on mobile platform especially for Indonesian users. This chatbot includes all services in the hotels and able to give the personalized services to the customers before arrive in the hotel. It can accommodate the customer needs and to up-selling the hotel facilities.
Dropout Induced Noise for Co-Creative GAN Systems

This paper demonstrates how Dropout can be used in Generative Adversarial Networks to generate multiple different outputs to one input. This method is thought as an alternative to latent space exploration, especially if constraints in the input should be preserved, like in A-to-B translation tasks.
Challenges in Adoption of Blockchain in Developing Countries

In today’s world of globalization where industries are becoming paperless, where robots are replacing human’s hard-core labor intensive work, where the whole world is connected with just one click, where predictive modeling and artificial intelligence AI is helping in tracking any big disaster and where biotech is helping in curing any disease in no time, Block-chain, one of the major innovations of today’s time, is all set to help humans in reaching to new heights of advancements in almost every field. But like other technologies, Blockchain is also facing some challenges, which must be addressed before widespread integration can take place. The mitigation these issues and all the obstacles in the adoption of Blockchain will have positive effects on the overall economy. This research, a questionnaire-based approach was adopted, aims to conclude and summarize the challenges faced by top-management of tech companies in the adoption of block-chain technology in developing countries.
Moving Object Distance Estimation Method Based on Target Extraction with a Stereo Camera

Real-time distance estimation with high accuracy is crucial to robot applications such as navigation and obstacle negotiation. However, considering the truth that high accuracy brings huge computation, the previous work either accelerates the distance estimation process through hardware, or only estimates the static objects, or sacrifices accuracy to realize real-time. In order to improve the trade-off between speed and accuracy, in this paper we propose a stereovision-based method that integrates the object detection and tracking into stereo matching. We utilize kernelized correlation filter (KCF) to track the moving object, and then extract target regions from image pair as input images to compute disparity map. In addition, we update each pixel's disparity by a simple but effective approach to further refine disparity map. Eventually, distance is calculated with disparity by triangulation. Experimental results show that our proposed method is suitable for dynamic environment, the extraction method improves the speed of the entire system, and the average accuracy of distance estimation can achieve over 95% from 88 cm to 300 cm when baseline is 151 mm.
VGMF: Visual contents and geographical influence enhanced point‐of‐interest recommendation in location‐based social network

Abstract Point‐of‐interest (POI) recommendation system has become an essential part of the location‐based social networks (LBSNs). Existing researches on POI recommendation mostly focus on geographical contents. However, the sparsity of user geographic data impedes the further improvement of POI recommendation performance. We have realized that for LBSNs, which are usually image‐heavy networks, the visual contents are significant clues for users' POI estimation. Based on the above, we propose a novel method to combine both geographical and visual contents for POI recommendation. The advantages of this method are threefold. First, through a fusion of both geographical and visual contents, we greatly alleviate the problem of sparse location data, which can significantly improve the recommendation performance. Second, we characterize the geographical clustering phenomenon more precisely, which can further improve the recommendation quality. Third, different from previous works that treat all locations where users have not visited as negative samples, we put forward an elaborate method to generate negative samples by calculating the inconsistency between users' preferable visual contents and location visual content. In the end, the problem of imbalance of positive and negative data is solved. We extensively evaluate the performance of our algorithm and other competitive baselines, and the evaluation results confirm the superiority of our method. Through the study of users' behaviors and some clues of users' preference in the POI recommendation, it will provide some instructions and references for the privacy protection in the LBSN security research. Furthermore, to render our experiments reproducible, the codes, and the dataset can be found at https://github.com/socialsnail/VGMF .
Design versus utility innovation: Is corruption sanding or greasing the wheels of innovation?

This paper studies whether corruption has a greasing or a sanding effect on the production of innovation. The main novelty lies in comparing the relative impacts on design versus utility innovation. On the one hand, corruption can undermine (sand) the expected returns from patents, whereas on the other hand, patent holders might be able to increase (grease) the scope and applicability of their patents. Using U.S. state‐level data and controlling for bidirectional causality, results show the sanding effect of corruption with regard to utility patents, with the negative effect on design patents occurring only with a time lag.
Some generalizations of the ASR search algorithm for quasitwisted codes

One of the most important and challenging problems in coding theory is explicit construction of linear codes with the best possible parameters. It is well known that the class of quasitwisted (QT) codes is asymptotically good and contains many linear codes with best known parameters (BKLCs). A search algorithm (ASR) on QT codes has been particularly effective to construct such codes. Recently, the ASR algorithm was generalized based on the notion of code equivalence. In this work, we introduce a new generalization of the ASR algorithm to include a broader scope of QT codes. As a result of implementing this algorithm, we have found eight new linear codes over the field 𝔽 5 . Furthermore, we have found seven additional new codes from the standard constructions of puncturing, shortening or Construction X. We also introduce a new search algorithm that can be viewed as a further generalization of ASR into the class multitwisted (MT) codes. Using this method, we have found many codes with best known parameters with more direct and desirable constructions than what is currently available in the database of BKLCs.
Analysis of Sentiments on Indonesian YouTube Video Comments: Case Study of The Indonesian Government's Plan to Move the Capital City

The Indonesian government's plan to move the capital city has received pros and cons in the community. Information related to it has become a trending topic in cyberspace, the internet, including YouTube. Many national news channels discuss this through videos on YouTube in the form of news, talk shows and interactive interviews with certain experts. Sentiment analysis that focuses on social issues is interesting to do on this topic. In contrast to the sentiment analysis carried out on product and service topics which usually uses a lot of adjective as the type of words analyzed to attract opinion sentiments, this study focuses on the verb as a focus to get the results of opinion sentiments related to social issues. With the Naive Bayes and Decision Tree to classifying sentiment, show that 91% and 93% is supporting the planned transfer of the capital city.