Reviewerly/exHarmony huggingface dataset:
Investigating Smartphone-based Pan and Zoom in 3D Data Spaces in Augmented Reality

In this paper, we investigate mobile devices as interactive controllers to support the exploration of 3D data spaces in head-mounted Augmented Reality (AR). In future mobile contexts, applications such as immersive analysis or ubiquitous information retrieval will involve large 3D data sets, which must be visualized in limited physical space. This necessitates efficient interaction techniques for 3D panning and zooming. Smartphones as additional input devices are promising because they are familiar and widely available in mobile usage contexts. They also allow more casual and discreet interaction compared to free-hand gestures or voice input. We introduce smartphone-based pan & zoom techniques for 3D data spaces and present a user study comparing five techniques. Our results show that spatial device gestures can outperform both touch-based techniques and hand gestures in terms of task completion times and user preference. We discuss our findings in detail and suggest suitable techniques for specific AR navigation tasks.
CB-CAS: Certificate-Based Efficient Signature Scheme With Compact Aggregation for Industrial Internet of Things Environment

The notion of aggregation of data in Industrial Internet of Things (IIoT) environment is a common practice. It shortens the data and associated signatures to reduce the bandwidth requirement. The compact aggregate signature (CAS) scheme creates a constant length aggregate signature (AS). Thus, the length of the CAS is independent of the number of messages or signatures to be aggregated. This article presents the first pairing-free CAS scheme in certificate-based settings. Due to the certificate-based approach, the proposed scheme is free from key escrow and key distribution problems inherited in identity-based cryptography (IDC) and certificate-less cryptography (CLC), respectively. Being compact and pairing free, it is the least bandwidth-consuming and the most efficient provably secure aggregation method. The length and computational cost analysis show that the scheme is the most appealing to use in the IIoT environment.
Multi-Person Tracking in Smart Surveillance System for Crowd Counting and Normal/Abnormal Events Detection

Automated video surveillance addresses people's real-time observation to describe their behaviors and interactions. This paper presents a novel multi-person tracking system for crowd counting and normal/ abnormal events detection at indoor/outdoor surveillance environments. The proposed system consists of four modules: people detection, head-torso template extraction, tracking and crowd cluster analysis. Firstly, the system extracts human silhouettes using inverse transform as well as median filter reducing the cost of computing and handling various complex monitoring situations. Secondly, people are detected by their head torso due to less varied and hardly occluded. Thirdly, each person is tracked through consecutive frames using the Kalman filter techniques with Jaccard similarity and normalized cross-correlation. Finally, the template marking is used for crowd counting having cues localization and clustered via Gaussian mapping for normal/abnormal events detection. The experimental results on two challenging datasets of video surveillance such as PETS2009 and UMN crowd analysis datasets demonstrate that the proposed system provides 88.7% and 95.5% in terms of counting accuracy and detection rate.
Indonesian Chatbot of University Admission Using a Question Answering System Based on Sequence-to-Sequence Model

Question and Answering (QA) system is a problem in natural language processing that can be used as the system of dialogs and chatbots. It can be used as a customer service that can provide a response to the customer quickly. A QA system receives an input in the form of sentences and produces the predictive sentences that are responses to the input. Therefore, a model that can learn such conversations is needed. This research focuses on developing a chatbot based on a sequence-to-sequence model. It is trained using a data set of conversation from a university admission. Evaluation on a small dataset obtained from the Telkom University admission on Whatsapp instant messaging application shows that the model produces a quite high BLEU score of 41.04. An attention mechanism technique using the reversed sentences improves the model to gives a higher BLEU up to 44.68.
SkrGAN: Sketching-Rendering Unconditional Generative Adversarial Networks for Medical Image Synthesis

Generative Adversarial Networks (GANs) have the capability of synthesizing images, which have been successfully applied to medical image synthesis tasks. However, most of existing methods merely consider the global contextual information and ignore the fine foreground structures, e.g., vessel, skeleton, which may contain diagnostic indicators for medical image analysis. Inspired by human painting procedure, which is composed of stroking and color rendering steps, we propose a Sketching-rendering Unconditional Generative Adversarial Network (SkrGAN) to introduce a sketch prior constraint to guide the medical image generation. In our SkrGAN, a sketch guidance module is utilized to generate a high quality structural sketch from random noise, then a color render mapping is used to embed the sketch-based representations and resemble the background appearances. Experimental results show that the proposed SkrGAN achieves the state-of-the-art results in synthesizing images for various image modalities, including retinal color fundus, X-Ray, Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). In addition, we also show that the performances of medical image segmentation method has been improved by using our synthesized images as data augmentation.
Docker Swarm and Kubernetes in Cloud Computing Environment

Distributed computing is a technology which contributes new stack of computing placed on virtualization of assets. With the most recent pattern of building up the applications on cloud, which empower the customer to get to it in an expanding way, by this the heap climbing quickly on the servers. Due to this sense of growing the stack on the servers, the resources are not efficiently exploited. So this is the logic why it has been brought in this era. The main ambition of us is to balance the work on each & every nodes. This will help to distribute the work on different nodes by remembering that no hubs ought to be overloaded. Thus this paper include what is docker and container by this clear cut ideas will helps us out to understand the docker swarm and kubernetes technology. Finally the paper shows how the services is been access by the node in a cluster with the help out docker swarm and kubernetes and also it provide the difference between them.
Learning Cross-Modal Deep Representations for Multi-Modal MR Image Segmentation

Multi-modal magnetic resonance imaging (MRI) is essential in clinics for comprehensive diagnosis and surgical planning. Nevertheless, the segmentation of multi-modal MR images tends to be time-consuming and challenging. Convolutional neural network (CNN)-based multi-modal MR image analysis commonly proceeds with multiple down-sampling streams fused at one or several layers. Although inspiring performance has been achieved, the feature fusion is usually conducted through simple summation or concatenation without optimization. In this work, we propose a supervised image fusion method to selectively fuse the useful information from different modalities and suppress the respective noise signals. Specifically, an attention block is introduced as guidance for the information selection. From the different modalities, one modality that contributes most to the results is selected as the master modality, which supervises the information selection of the other assistant modalities. The effectiveness of the proposed method is confirmed through breast mass segmentation in MR images of two modalities and better segmentation results are achieved compared to the state-of-the-art methods.
Pick-and-Learn: Automatic Quality Evaluation for Noisy-Labeled Image Segmentation

Deep learning methods have achieved promising performance in many areas, but they are still struggling with noisy-labeled images during the training process. Considering that the annotation quality indispensably relies on great expertise, the problem is even more crucial in the medical image domain. How to eliminate the disturbance from noisy labels for segmentation tasks without further annotations is still a significant challenge. In this paper, we introduce our label quality evaluation strategy for deep neural networks automatically assessing the quality of each label, which is not explicitly provided, and training on clean-annotated ones. We propose a solution for network automatically evaluating the relative quality of the labels in the training set and using good ones to tune the network parameters. We also design an overfitting control module to let the network maximally learn from the precise annotations during the training process. Experiments on the public biomedical image segmentation dataset have proved the method outperforms baseline methods and retains both high accuracy and good generalization at different noise levels.
Are We There Yet? Immersive Virtual Reality to Improve Cognitive Function in Dementia and Mild Cognitive Impairment

Abstract Background and Objectives Cognitive training therapies may delay cognitive deterioration in dementia. There is potential to enhance delivery through immersive virtual reality (IVR), as removing potential distractors for cognitively impaired individuals can enhance their experience, resulting in increased engagement. Evidence in this field is emerging and not yet synthesized. We aimed to summarize research investigating the use of IVR in dementia to evaluate the current extent of use, acceptability, feasibility, and potential effectiveness. We also aimed to identify gaps in current research and to create a set of recommendations in utilizing this therapy. Research Design and Methods A systematic literature review was conducted. Our review was registered with PROSPERO, registration number: CRD42019122295. We undertook searches of five databases, article references, and citations. Key authors in the field of health care VR were also contacted to identify additional papers. Articles were assessed for inclusion by two researchers independently. Data were extracted using standardized forms. Results Our search identified a total of 2,824 citations, following screening for duplicates and application of inclusion and exclusion criteria, five studies were included for analysis. Included studies were heterogeneous, with small sample sizes and mixed outcomes. Discussion and Implications We were unable to reach definitive conclusions over the use, acceptability, and effectiveness of IVR for dementia and mild cognitive impairment. Future studies should focus on ensuring their interventions are truly immersive, developing more robust controls and account for the rapid rate of obsolescence in digital technologies.
Neural Style Transfer Improves 3D Cardiovascular MR Image Segmentation on Inconsistent Data

Three-dimensional medical image segmentation is one of the most important problems in medical image analysis and plays a key role in downstream diagnosis and treatment. Recent years, deep neural networks have made groundbreaking success in medical image segmentation problem. However, due to the high variance in instrumental parameters, experimental protocols, and subject appearances, the generalization of deep learning models is often hindered by the inconsistency in medical images generated by different machines and hospitals. In this work, we present StyleSegor, an efficient and easy-to-use strategy to alleviate this inconsistency issue. Specifically, neural style transfer algorithm is applied to unlabeled data in order to minimize the differences in image properties including brightness, contrast, texture, etc. between the labeled and unlabeled data. We also apply probabilistic adjustment on the network output and integrate multiple predictions through ensemble learning. On a publicly available whole heart segmentation benchmarking dataset from MICCAI HVSMR 2016 challenge, we have demonstrated an elevated dice accuracy surpassing current state-of-the-art method and notably, an improvement of the total score by 29.91%. StyleSegor is thus corroborated to be an accurate tool for 3D whole heart segmentation especially on highly inconsistent data, and is available at https://github.com/horsepurve/StyleSegor .
Nuclei Segmentation in Histopathological Images Using Two-Stage Learning

Nuclei segmentation is a fundamental and important task in histopathological image analysis. However, it still has some challenges such as difficulty in segmenting the overlapping or touching nuclei, and limited ability of generalization to different organs and tissue types. In this paper, we propose a novel nuclei segmentation approach based on a two-stage learning framework and Deep Layer Aggregation (DLA). We convert the original binary segmentation task into a two-step task by adding nuclei-boundary prediction (3-classes) as an intermediate step. To solve our two-step task, we design a two-stage learning framework by stacking two U-Nets. The first stage estimates nuclei and their coarse boundaries while the second stage outputs the final fine-grained segmentation map. Furthermore, we also extend the U-Nets with DLA by iteratively merging features across different levels. We evaluate our proposed method on two public diverse nuclei datasets. The experimental results show that our proposed approach outperforms many standard segmentation architectures and recently proposed nuclei segmentation methods, and can be easily generalized across different cell types in various organs.
Crypto-Currency price prediction using Decision Tree and Regression techniques

Crypto-currency such as Bitcoin is more popular these days among investors. In the proposed work, it is studied to forecast the Bitcoin price precisely considering different parameters that influence the Bitcoin price. This study first handles, it is identified the price trend on day by day changes in the Bitcoin price while it gives knowledge about Bitcoin price trends. The dataset till current date is taken with open, high, low and close price details of Bitcoin value. Exploiting the dataset machine learning module is introduced for prediction of price values. The aim of this work is to derive the accuracy of Bitcoin prediction using different machine learning algorithm and compare their accuracy. Experiment results are compared for decision tree and regression model.
Survey on complex ontology matching

Simple ontology alignments, largely studied in the literature, link a single entity of a source ontology to a single entity of a target ontology. A limitation of these alignments is their lack of expressiveness which can be overcome by complex alignments. While diverse state-of-the-art surveys main ly review the matching approaches in general, to the best of our knowledge, there is no study of the specificities of the complex matching problem. In this paper, a review of the different complex matching approaches is provided. It proposes a classification of the complex matching approaches based on their specificities (i.e., type of correspondences, guiding structure). The evaluation aspects and the limitations of these approaches are also discussed. Insights for future work in the field are provided.
Automated Planning for Robotics

Modern robots are increasingly capable of performing “basic” activities such as localization, navigation, and motion planning. However, for a robot to be considered intelligent, we would like it to be able to automatically combine these capabilities in order to achieve a high-level goal. The field of automated planning (sometimes called AI planning) deals with automatically synthesizing plans that combine basic actions to achieve a high-level goal. In this article, we focus on the intersection of automated planning and robotics and discuss some of the challenges and tools available to employ automated planning in controlling robots. We review different types of planning formalisms and discuss their advantages and limitations, especially in the context of planning robot actions. We conclude with a brief guide aimed at helping roboticists choose the right planning model to endow a robot with planning capabilities.
Automated Video Game Testing Using Synthetic and Humanlike Agents

In this article, we present a new methodology that employs tester agents to automate video game testing. We introduce two types of agents - synthetic and humanlike - and two distinct approaches to create them. Our agents are derived from Sarsa and Monte Carlo tree search (MCTS) but focus on finding defects, while traditional game-playing agents focus on maximizing game scores. The synthetic agent uses test goals generated from game scenarios, and these goals are further modified to examine the effects of unintended game transitions. The humanlike agent uses test goals extracted by our proposed multiple greedy-policy inverse reinforcement learning (MGP-IRL) algorithm from tester trajectories. MGP-IRL captures multiple policies executed by human testers. We use our agents to produce test sequences, and run the game with these sequences. At each run, we use an automated test oracle to check for bugs. We analyze the proposed method in two parts-we compare the success of humanlike and synthetic agents in bug finding, and we evaluate the similarity between humanlike agents and human testers. We collected 427 trajectories from human testers using the General Video Game Artificial Intelligence (GVG-AI) framework and created three games with 12 levels that contain 45 bugs. Our experiments reveal that humanlike and synthetic agents compete with human testers' bug finding performances. Moreover, we show that MGP-IRL increases the humanlikeness of agents while improving the bug finding performance.
A Quantitative Study on Characteristics and Effect of Batch Refactoring on Code Smells

Background: Code refactoring aims to improve code structures via code transformations. A single transformation rarely suffices to fully remove code smells that reveal poor code structures. Most transformations are applied in batches, i.e. sets of interrelated transformations, rather than in isolation. Nevertheless, empirical knowledge on batch application, or batch refactoring, is scarce. Such scarceness helps little to improve current refactoring practices. Aims: We analyzed 57 open and closed software projects. We aimed to understand batch application from two perspectives: characteristics that typically constitute a batch (e.g., the variety of transformation types employed), and the batch effect on smells. Method: We analyzed 19 smell types and 13 transformation types. We identified 4,607 batches, each applied by the same developer on the same code element (method or class); we expected to have batches whose transformations are closely interrelated. We computed (1) the frequency in which five batch characteristic manifest, (2) the probability of each batch characteristics to remove smells, and (3) the frequency in which batches introduce and remove smells. Results: Most batches are quite simple: although most batches are applied on more than one method (90%), they are usually composed of the same transformation type (72%) and only two transformations (57%). Batches applied on a single method are 2.6 times more prone to fully remove smells than batches affecting more than one method. Surprisingly, batches mostly ended up introducing (51%) or not fully removing (38%) smells. Conclusions: The batch simplicity suggests that developers have sub-explored the combinations of transformations within a batch. We summarized some batches that may fully remove smells, so that developers can incorporate them into current refactoring practices.
A Multidimensional Choledoch Database and Benchmarks for Cholangiocarcinoma Diagnosis

Histopathological examination is very important for diseases diagnosis and treatment.With the development of artificial intelligence, more and more pathological databases have been reported for histopathological diagnosis because database is quite crucial for the validation and testing of feature extraction, statistical analysis and deep learning algorithms.However, most of these databases are either gray images or RGB color images of tissue sections contain limited information of samples which limited the performance of most current deep learning algorithms.There are few publicly available pathological databases that include more than two modalities for the same subject.This paper introduces a database for both microscopy hyperspectral and color images of cholangiocarcinoma, including 880 scenes from 174 individuals, among which 689 scenes are samples with part of cancer areas, 49 scenes full of cancer areas, and 142 scenes without cancer areas.In addition, all cancer areas have been precisely labeled by experienced pathologists.The contributions of this work: a) A comprehensive and up-to-date review on pathological imaging systems and databases; b) Detailed description of the proposed the multidimensional Choledoch Database and login method; c) The multidimensional Choledoch
Efficient and Provably Secure Multireceiver Signcryption Scheme for Multicast Communication in Edge Computing

With the popularity of edge computing, edge nodes are connected with the Internet of Things (IoT) devices to process and analyze IoT-created data, and feedback corresponding results to users, devices, or data centers. In the edge computing environment, multicast is a typical communication pattern to support data transmitting between edges and devices. It allows the sender to send messages to multiple receivers in one broadcast message. To construct a secure multicast channel, the primary issue is to ensure the privacy and credibility of the transmitted message in the open wireless communication. Then, another essential issue for multicast channels is receiver anonymity, i.e., only the sender knows the receivers' identities. Also, efficiency and provable security are critical in scheme design. In this article, we design a certificateless multimessage and multireceiver signcryption (CLMMSC) scheme by using the elliptic curve cryptography. To facilitate lightweight deployment, we adapt the certificateless mechanism to reduce the system operation and maintenance costs. Then, through security proofs, we demonstrate that the proposed scheme can achieve the expected security properties. The performance analysis shows that the proposed scheme has lower communication costs than previous CLMMSC schemes.
Convolutional neural networks

In this chapter we introduce convolutional neural networks by starting with multilinear perceptrons, and proceed by explaining backpropagation. Using this we proceed to convolutional neural networks, explain the concept of convolutions, and provide practical methodologies to train such networks in the classification and segmentation setting.