Reviewerly/exHarmony huggingface dataset:
Medical images performance analysis and observations with SPIHT and wavelet techniques

The medical image compression plays an important role in digital image compression. Compression reduces the redundancy and encodes the data. To use different techniques and algorithms we can get the desired result. But it will depend on the used algorithm, which algorithm we have used in which circumstances. In this paper we have used SPIHT method with wavelet bior4.4 on brain surface medical image with different type of image parameters. And observe the result of this method with different types of image parameters at different level of iterations.
Accurate And Efficient Copy-Move Forgery Detection

Today, due to the development of image processing applications like GIMP and Photoshop, forging of digital images with the absence of any trace is not at all difficult. Therefore crucial validation of content in a digital image is essential. Among the many prevailing forgery techniques, the copy-move forgery is recurrently occurring and it is easily performed. Detection of copy-move forgery is difficult as the forged region show similar properties to the source image. This paper analyzes the issue of copy-move forgeries and proposes an effective and reliable technique for its identification. Basically for the detection of copy-move forgery, two main approaches are applicable - block based and keypoint based techniques. Block-based technique employs division of image matrix into blocks that overlap and features are extracted. In keypoint based approach features of each keypoint is extracted for further matching. The aim is to implement two algorithms for detecting copy-move forgery - one using block based Discrete Cosine Transform algorithm and the other using the keypoint-based Scale Invariant Feature Transform. Efficiency of both algorithms are measured and their comparison is done using the GRIP and CoMoFoD databases. It was found that the Scale Invariant Feature Transform has more accuracy and precision compared to Discrete Cosine Transform method.
Abnormal Detection of Substation environment based on Improved YOLOv3

In order to satisfy the requirements of power operation scene safety monitoring and lessen the human accidents, a novel framework of video surveillance for abnormal detection in substations based on artificial technology was presented. Firstly, a multi-classes detector based on improved YOLOv3 was proposed to realize the detection and localization of multiple targets. Then, types of violation and defect were discriminated by secondary analysis for the workers or equipments based on detection results. Substation safety monitoring system was developed to implement environment abnormal detection, and it improved the automation and intelligentization level of unmanned substation. The experimental results demonstrate that the mAP(Mean Average Precision) reaches 0.85, which is slightly improved compared with YOLOv3. Nevertheless, the proposed algorithm is capable of real-time detection as faster than 15 frames per second (fps).
K-Nearest Neighbors Undersampling as Balancing Data for Cyber Troll Detection

Cyber Troll is a behavior to provoke and attack one's emotion online. It happens throughout the world, including Indonesia. One of the social media platforms in which cyber trolling frequently happen is Twitter. There are several ways to deal with cyber trolling, one of which is to implement Support Vector Machine Classifier to separate between cyber troll tweets and normal tweets. Since most of the tweets are normal tweets, it is hard to obtain a balance between them and this leaves us at 1:2 ratio or even more. Just like most machine learning algorithm, SVM can show poor performance on the minority class because SVM was designed to induce a model based on the overall error. In this paper, we tried to implement KNN-Undersampling method before the actual classifying process to deal with imbalanced data and compare them to SVM with SMOTE and normal SVM. The highest accuracy using a combination between SVM and KNN- Undersampling reached 63.83% which give slightly better result compared to a combination between SVM and SMOTE or normal SVM.
Named Entity Location Prediction Combining Twitter and Web

Knowledge bases are critical to many applications. However, they are greatly incomplete. Enriching knowledge bases with new entities and new location attributes becomes increasingly important. Given a named entity with tweets and Web documents where the entity appears, we aim to predict the entity city-level location combining the geographical location knowledge embedded in both Twitter and Web. This task is helpful for knowledge base enrichment and tweet location prediction. In this paper we propose NELPTW, the first unsupervised framework for <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>N</b></u> amed <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>E</b></u> ntity <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>L</b></u> ocation <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>P</b></u> rediction by leveraging the knowledge from <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>T</b></u> witter and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>W</b></u> eb. Based on each data source, NELPTW utilizes a linear function ranking model to generate several rankings to the candidate location set for each entity. To combine the knowledge from two sources which have different reliability and importance for the location prediction, an unsupervised rank aggregation algorithm is developed to aggregate multiple rankings for each entity to obtain a better ranking. A learning algorithm based on the EM method is proposed to automatically learn the parameters of the ranking model without requiring any training labels. The experimental results over a real world Twitter and Web data set show that our framework significantly outperforms the baselines in terms of accuracy.
Discriminative Face Hallucination via Locality-Constrained and Category Embedding Representation

Recent years have witnessed the rapid development of face image hallucination techniques. However, the previous face hallucination methods are unsupervised and ignore the label information of training samples, leading to undesirable results. This article proposes a locality-constrained and category embedding representation (LCER) method to super-resolve face image in a supervised manner by embedding the label information in data representation. The proposed LCER incorporates the locality prior and category information into one unified framework, which aims to learn both the advantages of locality in preserving the true typologic structure of data manifold and the discriminability in exposing the class subspace information. Such strategy allows the LCER not only to preserve more sharpen image details but also to guarantee the face structure pattern be transferred mainly from the same subject in super-resolution reconstruction. Extensive experiments were conducted to evaluate the proposed LCER, and the comparative results demonstrate that it achieved superior face hallucination performance in both the quantitative measurements and visual impressions compared to several state-of-the-art.
Feasible NPC Hiding Behaviour using Goal Oriented Action Planning in case of Hide-and-Seek 3D Game Simulation

Non-Playable Characters (NPC) in the digital game are a necessary component to make the game mechanic more attractive. One common feature of most NPC in games is NPC that can hide, especially in stealth-oriented games. NPC needs to stay hidden to avoid players or something. The hiding strategies on NPC is more challenging to build related to the complexity of the game mechanic. Active hiding situation can found on hide-and-seek game. NPC as a hider in hide-and-seek game has complex behavior such as observing seeker, finding the safe place, moving to another hiding place, securing itself after being discovered by the seeker, and deciding to run away to the base. In this work, we proposed to use Goal-Oriented Action Planning (GOAP) with placement limitation (maximum two NPC in one hiding place) and unlimited placement to modeling the NPC hiding behavior. The GOAP NPC is effective to make hiding strategies through hide-and-seek game simulation.
Augmented Reality Application for Science Education on Animal Classification

At present, learning media for the introduction of animal classifications based on food types have been applied in education today using books as learning tools. Student learning process is directed at the student's ability to memorize information, the student's brain is forced to remember and hoard various information obtained from the educator. Therefore, based on the issue, an Augmented Reality technology is then proposed to create an interesting real-time 3-dimensional (3D) animation. The animations were built using Blender while the AR development process uses the Vuforia SDK. This application displays animal objects that are classified by type of food such as carnivores, herbivores, and omnivores. These results are then tested on an elementary school students (7-9 years old). The results obtained from 20 respondents explained that this application can help students understand the classification of animal groups by type of food and can be used as an alternative multimedia learning solution about animal classification.
Feature Selection Using Flower Pollination Optimization to Diagnose Lung Cancer from CT Images

A segmentation approach using snake splines and a flower pollination based feature selection are suggested in this work to diagnose lung cancer from Computed Tomography scan images. The proposed system segments the lung tissues including the diseased portion using a prior shape model generated using snake splines. The model generation includes the usage of control points to construct a snake spline model. The model is scaled on to the diseased lung tissues so as to segment the lung region along with its boundary. The level of scaling varies depending on the size of reference and diseased lung image and hence requires certain affine transformations to fit. From the segmented lung, the cancerous regions which are the region of interests (ROIs) are extracted. Features are extracted from these ROIs. A binary flower pollination algorithm is used to select the relevant features in wrapper approach for further classification using SVM classifier. In this work, 514 ROIs are extracted among which 414 ROIs are used for training and 100 for testing in relation to 80:20 Pareto principle. 33 features are selected by binary flower pollination algorithm from 56 extracted features. The proposed approach achieved an accuracy of 84% using SVM classifier. The results obtained when compared with similar algorithms, either selected more than 33 features or yielded a lower accuracy.
CBFNet: Constraint balance factor for semantic segmentation

The deep neural network model based on spatial pyramid pooling for multi-scale context information has been widely adopted in image semantic segmentation. However, this structure reduces the feature resolution during the inference process, which should be recovered by bilinear interpolation. We find that the performance of boundary location heavily depends on the interpolation operation. Assuming that the foreground object has a boundary with uneven curvature changes, it will be difficult to preserve details in the lower resolution feature map. In this case, if the model achieves near-optimal fitting, the prediction of the boundary point needs to satisfy strict constraints. But this condition is very difficult to meet for the model. For this purpose, we propose a constraint balance factor (CBF) relaxation method to improve the fitting ability of the model. The constraint balance factor is learned end-to-end by a sub-network. This sub-network can be easily embedded into a semantic segmentation network that requires interpolation without too many extra parameter increments. The experimental results illustrate that the proposed CBFNet performs well on PASCAL VOC 2012 and Cityscapes datasets.
Advanced Intersection over Union Loss for Visual Tracking

Intersection over Union (IoU) is the most important metric in visual tracking benchmark. However, IoU cannot always accurately describe the similarity between two bounding boxes. In some cases, IoU cannot reflect the similarity of location, shape (aspect ratio) and area between two bounding boxes correctly, which means even if two group bounding boxes have same IoU, their positions, shapes and areas deviation may be different. In this paper, we propose a new evaluation metric, called Advanced Intersection over Union (AIoU), to solve this problem by adding penalties for positions, shapes and areas changes between two bounding boxes, and apply AIoU as a loss function to the bounding box regression part of Siamese tracker. By training the regression branch of Siamese tracker with AIoU loss, IoU loss and traditional minimum Mean Square Error (MSE) loss function, we show that the new AIoU loss is more effective for locating than MSE loss and IoU loss on VOT benchmark. At the same time, we combine SiamRPN with AIoU loss to form the SiamAIoU tracker and compare our method with many state-of-the-art trackers, the results show that SiamAIoU get higher scores on OTB100, VOT2016 and VOT2018. In addition, our tracker runs at 35 FPS in real time.
A strategy of multi-UAV cooperative path planning based on CCPSO

Multi-UAV cooperative formation flying has significant advantages in broadening the scope of UAV operations and improving combat effectiveness. How to realize the cooperative path planning of multi-UAV is an open issue. In order to solve the search performance degradation as dimension increases, a strategy of multi-UAV cooperative path planning based on Cooperative Coevolution Particle Swarm Optimization (CCPSO) is proposed in this paper. Firstly, the initial Dubins path is generated for each UAV in the formation, of which the longest one is selected as the reference path. Intermediate waypoints are designed to modify the initial paths to avoid collisions with obstacles. Then, CCPSO algorithm is applied to optimize path parameters and adjust the path length to be equal to the reference path to ensure time coordination. Collision avoidance between UAVs is achieved in the collaborative evaluation phase. Finally, an formation rendezvous example and comparison with CCGA are given to explain the validity and excellent search ability of the proposed method.
Digital library as learning resources

Abstract Information and Communications technology (ICT) has been widely applied in digital library services to improve the knowledge and the public interest. Researchers can easily obtain the information of interest through internet surfing. Digital libraries seek to provide prospective readership with alternative materials for reading. They also preserve the culture, customs that could be learnt by future generations. The utilization of digital libraries as learning resources include educational and community aspects. The purpose of this paper was to review and analyse the impact of a digital library system in education on potential readership.
A Multi-channel Neural Network for Imbalanced Emotion Recognition

Imbalanced issue becomes one of major bottleneck for further popularizing of emotion recognition in actual applications. Recently, some resampling methods have been proposed to improve performance by balancing the training samples. However, over-sampling methods may lead to overfitting, and undersampling methods would lose useful emotion information. In this paper, we propose a multi-channel deep architecture to improve performance in both samples and features imbalance. Specifically, we design a class correction loss function to overcome the gap between majority and minority emotions. Meanwhile, emotionspecific word embedding and a fine-tuning BERT are used to increase the differentiation of emotion words and sentences. Experimental results on two Chinese micro-blog emotion classification datasets show that our proposed architecture outperforms state-of-the-art in imbalanced emotion recognition.
Analysis of Learning Approaches for Machine Translation Systems

Machine Translation(MT) is a part of Natural Language Processing(NLP). It is the method of translating Source Language(SL) text into Target Language(TL). The gap between computer programmer and linguist can be resolved with this system. The problems like lexical ambiguity, part of speech tagging, syntactic and structural ambiguity, synonym etc will arise while developing such systems. To develop a proper bilingual machine translation system for two natural languages is a challenging and demanding task for researchers. It is required to analyze the information as well as technology behind every natural language translation. This work represents the various approaches with current trends of machine translation system. In recent trends various machine learning approaches have been developed to tackle the above said problems. Most recently neural machine translation attain very good result by using different deep neural network techniques and machine learning algorithms.
Comparative Study of Various Image Captioning Models

This paper briefly tells about the different models used for image captioning. It also discusses how the progress in the areas of machine translation and object recognition which has eventually enhanced the performance of image captioning model greatly in current time. Implementation of models are also being discussed in this paper. In the end, standard evaluation matrices have been used to evaluate the performance of the models.
Emojinating

Emoji are pictorial symbols encoded in the Unicode Standard and thus available in most of writing systems. The popularity of emoji is constantly increasing among both users and brands, used to represent more concepts each year. Due to the connection between their pictorial representation and associated semantic knowledge, they can be computationally exploited. Emojinating is a co-creative system that produces visual representation of concepts by visually blending existing emoji. This paper presents an installation that offers a sneak peek at the different stages of the co-creative process: co-creation, production and interpretation. It has a dual nature, being composed of videos that show the interaction between artist and computer, and a set of four finished artworks under the theme "Addictive behaviors in the digital Era".
An improved step counting algorithm using classification and double autocorrelation

The objective of this paper was to develop an end-to-end algorithm that would improve the step counting accuracy in regular walking/running data and also meet the ANSI/CTA-2056 standards. The ANSI/CTA-2056 standards are to achieve an error rate of less than 10% on treadmill data on at least 20 participants. Our UWF-algorithm (UWFv1) has an improved step counting accuracy and also performs well below the acceptable ANSI/CTA-2056 error rate, using both treadmill data and non-treadmill data, hence our UWFv1 algorithm also meets the ANSI-CTA-2056 standards. For the end-to-end algorithm, the random forest model, trained on a feature engineered dataset, was chosen as the walking/running detection classifier, and double autocorrelation was recommended in the process of determining the step counts.
Blockchain for Cybersecurity: Working Mechanism, Application areas and Security Challenges

Blockchain is receiving huge attention from researchers and various organizations as it brings forth magical solutions to the classical centralized architecture problems. Blockchain, either private or public, is a kind of distributed ledger for maintenance of integrity among various transactions by the process of ledger decentralization among the participating end users. On the contrary, Internet of Things (IoT) is the internet revolution that facilitates connection of all end user devices over the internet so that they can share their services and applications for improvement of daily living standards. Centralized IoT provides numerous benefits, however, it also brings forth several security challenges. Integrating blockchain with IoT is an effective way to resolve these security challenges. This paper presents a detailed review that highlights this integration process. The paper presents the working mechanism of the blockchain and how it changes the nature of value. Application areas and security challenges are also outlined and discussed in the paper.
Contextual Aware Dynamic Healthcare Service Composition Based on Semantic Web Ontology

Web service composition is a novel methodology to integrate diverse business solutions to accomplish complex business logic. In this fast-paced digital era with diverse categories of users, intelligent service composition based on the contextual information is highly essential to satisfy individual users requirements. For intelligent composition, ontology based declarative knowledge model is used to describe the business rules, commercial directions and personalized user environment in machine comprehensible way. This paper depicts healthcare service composition with domain ontology to encompass ontology manipulation, contextual information gathering, selecting services and dynamic composition. E-healthcare system automates service composition which invokes hospital, ambulance, scan-laboratory and pharmacy services. It provide valuable information like nearest hospital, best ambulance, scan-laboratory facilities, appropriate department and doctor details by gathering the contextual information of users like location, severity, symptoms, etc. Thus, elaborate search process is eliminated which ultimately helps the user to save time and life in case of emergency situations. thod.
A Study on the Design and Presentation of Virtual Contents in AR Picture Books

Today, augmented reality has been applied to a variety of areas, including AR picture books. The incorporation of AR into picture books brings readers a new way of reading picture books and is gaining its popularity among readers. However, as the most important feature of AR picture books, virtual contents have not been properly used, and there is severe chaos in terms of the presentation. The virtual contents and the narrative of picture books are disjointed, which affects the reading experience of the readers. In this paper, with the purpose of providing better reading experience of AR picture books to readers, the design form of virtual content in AR picture book is described, the features of reading reflected through the incorporation of the forms of virtual contents and narratives of picture books are pointed out, and the ways that virtual contents are integrated with narratives of picture books in terms of design and presentation are discussed.
A Classifier Approach using Deep Learning for Human Activity Recognition

Research in the field of Human Activity has been motivated by a number of factors. Whether it is to derive human based activity behavior for 3D models, or for identity recognition using gait, or for medical purposes, HAR is indispensable. Our research proposal a generic framework for recognition of various human activities namely- Jogging, Walking, Sitting, Standing, Upstairs, Downstairs. We applied a number of standard machine learning algorithms and have got the best results by using the Random Forest algorithm approach. Human Activity Recognition is an important aspect of computer vision and its applications. It is highly significant, as it gives us an automated analysis of occurring events and their context. Our model gives us the best accuracy of 98%.