Reviewerly/exHarmony huggingface dataset:
Object Instance Annotation With Deep Extreme Level Set Evolution

In this paper, we tackle the task of interactive object segmentation. We revive the old ideas on level set segmentation which framed object annotation as curve evolution. Carefully designed energy functions ensured that the curve was well aligned with image boundaries, and generally "well behaved". The Level Set Method can handle objects with complex shapes and topological changes such as merging and splitting, thus able to deal with occluded objects and objects with holes. We propose Deep Extreme Level Set Evolution that combines powerful CNN models with level set optimization in an end-to-end fashion. Our method learns to predict evolution parameters conditioned on the image and evolves the predicted initial contour to produce the final result. We make our model interactive by incorporating user clicks on the extreme boundary points, following DEXTR. We show that our approach significantly outperforms DEXTR on the static Cityscapes dataset and the video segmentation benchmark DAVIS, and performs on par on PASCAL and SBD.
Real-Time Smart Attendance System using Face Recognition Techniques

The management of the attendance can be a great burden on the teachers if it is done by hand. To resolve this problem, smart and auto attendance management system is being utilized. But authentication is an important issue in this system. The smart attendance system is generally executed with the help of biometrics. Face recognition is one of the biometric methods to improve this system. Being a prime feature of biometric verification, facial recognition is being used enormously in several such applications, like video monitoring and CCTV footage system, an interaction between computer & humans and access systems present indoors and network security. By utilizing this framework, the problem of proxies and students being marked present even though they are not physically present can easily be solved. The main implementation steps used in this type of system are face detection and recognizing the detected face.This paper proposes a model for implementing an automated attendance management system for students of a class by making use of face recognition technique, by using Eigenface values, Principle Component Analysis (PCA) and Convolutional Neural Network (CNN). After these, the connection of recognized faces ought to be conceivable by comparing with the database containing student's faces. This model will be a successful technique to manage the attendance and records of students.
Voice assistants and older people

Voice Assistants (VAs) like Amazon Echo and Apple Siri are an increasingly popular way of interacting with a range of applications. VAs are also currently gaining traction in the HCI community. Yet, and despite a growing ageing population, work on VAs with older people is scant. In this CUI 2019 provocative paper we aim to encourage research on VAs with and for older people (aged 65+). We outline several important open issues to address when researching this population, such as perceptions and barriers to VAs use, aspects of Conversational User Experience tied to VAs response design, and anthropomorphic design. We also raise some 'provocative' and yet-to-be-addressed research questions, hoping to operationalize the issues discussed and spark debate and discussion about them during and after CUI 2019.
Blockchain technology in commercial real estate transactions

Purpose The transaction process of an office building is known to be time consuming and inefficient, in part due to the lack of market transparency. The purpose of this paper is to focus on the development of a blockchain application that can improve the transaction process of office buildings in the Netherlands. Design/methodology/approach Conducting design science research, the current transaction process of an office building and status quo of blockchain technology in real estate is investigated. Subsequently, multiple parties are interviewed to define major pain points within the process. The interview findings are used to design a blockchain solution which overcomes the aforementioned pain points. After designing, the interviewees are asked again to pragmatically validate the proposed model. Findings One of the major pain points identified concerning the transaction process of an office building is that it is difficult to define the characteristics of a property, due to lack of data structure and quality. The proposed application improves the way specific assets are understood by structuring physical and contractual information in one place and guarantees the quality of the data by using the blockchain mechanisms. Practical implications A blockchain application is proposed, which can improve the transaction process of an office building. Originality/value Recent studies indicate that blockchain technology could lead to improvements in efficiency, transparency and therefore trust within the transaction process. Therefore, the proposed application is of value for the future of real estate data management and the transaction process.
Credit Card Fraud Detection Using Random Forest Algorithm

In this paper we mainly focus on credit card fraud detection in real world. Here the credit card fraud detection is based on fraudulent transactions. Generally credit card fraud activities can happen in both online and offline. But in today's world online fraud transaction activities are increasing day by day. So in order to find the online fraud transactions various methods have been used in existing system. In proposed system we use Random Forest Algorithm(RFA) for finding the fraudulent transactions and the accuracy of those transactions. This algorithm is based on supervised learning algorithm where it uses decision trees for classification of the dataset. After classification of dataset a confusion matrix is obtained. The performance of Random Forest Algorithm is evaluated based on the confusion matrix. The results obtained from processing the dataset gives accuracy of about 90%.
Mobile Robot Navigation based on Deep Reinforcement Learning

Learning to navigate in an unknown environment is a crucial capability of mobile robot. Conventional method for robot navigation consists of three steps, involving localization, map building and path planning. However, most of the conventional navigation methods rely on obstacle map, and dont have the ability of autonomous learning. In contrast to the traditional approach, we propose an end-to-end approach in this paper using deep reinforcement learning for the navigation of mobile robots in an unknown environment. Based on dueling network architectures for deep reinforcement learning (Dueling DQN) and deep reinforcement learning with double q learning (Double DQN), a dueling architecture based double deep q network (D3QN) is adapted in this paper. Through D3QN algorithm, mobile robot can learn the environment knowledge gradually through its wonder and learn to navigate to the target destination autonomous with an RGB-D camera only. The experiment results show that mobile robot can reach to the desired targets without colliding with any obstacles.
Co-Saliency Detection via Mask-Guided Fully Convolutional Networks With Multi-Scale Label Smoothing

In image co-saliency detection problem, one critical issue is how to model the concurrent pattern of the co-salient parts, which appears both within each image and across all the relevant images. In this paper, we propose a hierarchical image co-saliency detection framework as a coarse to fine strategy to capture this pattern. We first propose a mask-guided fully convolutional network structure to generate the initial co-saliency detection result. The mask is used for background removal and it is learned from the high-level feature response maps of the pre-trained VGG-net output. We next propose a multi-scale label smoothing model to further refine the detection result. The proposed model jointly optimizes the label smoothness of pixels and superpixels. Experiment results on three popular image co-saliency detection benchmark datasets including iCoseg, MSRC and Cosal2015 demonstrate the remarkable performance compared with the state-of-the-art methods.
BCOSN: A Blockchain-Based Decentralized Online Social Network

Online social networks (OSNs) are becoming more and more prevalent in people's life, but they face the problem of privacy leakage due to the centralized data management mechanism. The emergence of distributed OSNs (DOSNs) can solve this privacy issue, yet they bring inefficiencies in providing the main functionalities, such as access control and data availability. In this article, in view of the above-mentioned challenges encountered in OSNs and DOSNs, we exploit the emerging blockchain technique to design a new DOSN framework that integrates the advantages of both traditional centralized OSNs and DOSNs. By combining smart contracts, we use the blockchain as a trusted server to provide central control services. Meanwhile, we separate the storage services so that users have complete control over their data. In the experiment, we use real-world data sets to verify the effectiveness of the proposed framework.
Auto-weighted multi-view co-clustering with bipartite graphs

Co-clustering aims to explore coherent patterns by simultaneously clustering samples and features of data. Several co-clustering methods have been proposed in the past decades. However, in real-world applications, datasets are often with multiple modalities or composed of multiple representations (i.e., views), which provide different yet complementary information. Hence, it is essential to develop multi-view co-clustering models to solve the multi-view application problems. In this paper, a novel multi-view co-clustering method based on bipartite graphs is proposed. To make use of the duality between samples and features of multi-view data, a bipartite graph for each view is constructed such that the co-occurring structure of data can be extracted. The key point of utilizing the bipartite graphs to deal with the multi-view co-clustering task is to reasonably integrate these bipartite graphs and obtain an optimal consensus one. As for this point, the proposed method can learn an optimal weight for each bipartite graph automatically without introducing an additive parameter as previous methods do. Furthermore, an efficient algorithm is proposed to optimize this model with theoretically guaranteed convergence. Extensive experimental results on both toy data and several benchmark datasets have demonstrated the effectiveness of the proposed model.
ASSD: Attentive single shot multibox detector

This paper proposes a new deep neural network for object detection. The proposed network, termed ASSD, builds feature relations in the spatial space of the feature map. With the global relation information, ASSD learns to highlight useful regions on the feature maps while suppressing the irrelevant information, thereby providing reliable guidance for object detection. Compared to methods that rely on complicated CNN layers to refine the feature maps, ASSD is simple in design and is computationally efficient. Experimental results show that ASSD competes favorably with the state-of-the-arts, including SSD, DSSD, FSSD and RetinaNet. Code is available at: .
Multi-scale Coarse-to-Fine Segmentation for Screening Pancreatic Ductal Adenocarcinoma

We propose an intuitive approach of detecting pancreatic ductal adenocarcinoma (PDAC), the most common type of pancreatic cancer, by checking abdominal CT scans. Our idea is named multi-scale segmentation-for-classification, which classifies volumes by checking if at least a sufficient number of voxels is segmented as tumors, by which we can provide radiologists with tumor locations. In order to deal with tumors with different scales, we train and test our volumetric segmentation networks with multi-scale inputs in a coarse-to-fine flowchart. A post-processing module is used to filter out outliers and reduce false alarms. We collect a new dataset containing 439 CT scans, in which 136 cases were diagnosed with PDAC and 303 cases are normal, which is the largest set for PDAC tumors to the best of our knowledge. To offer the best trade-off between sensitivity and specificity, our proposed framework reports a sensitivity of $$94.1\%$$ at a specificity of $$98.5\%$$ , which demonstrates the potential to make a clinical impact.
Inception and ResNet features are (almost) equivalent

Deep convolutional neural networks (CNNs) are the dominant technology in computer vision today. Much of the recent computer vision literature can be thought of as a competition to find the best architecture for vision within the deep convolutional framework. Despite all the effort invested in developing sophisticated convolutional architectures, however, it’s not clear how different from each other the best CNNs really are. This paper measures the similarity between two well-known CNNs, Inception and ResNet, in terms of the properties they extract from images. We find that the properties extracted by Inception are very similar to the properties extracted by ResNet, in the sense that either feature set can be well approximated by an affine transformation of the other. In particular, we find evidence that the information extracted from images by ResNet is also extracted by Inception, and in some cases may be more robustly extracted by Inception. In the other direction, most but not all of the information extracted by Inception is also extracted by ResNet. The similarity between Inception and ResNet features is surprising. Convolutional neural networks learn complex non-linear features of images, and the architectural differences between systems suggest that these non-linear functions should take different forms. Nonetheless, Inception and ResNet were trained on the same data set and seem to have learned to extract similar properties from images. In essence, their training algorithms hill-climb in totally different spaces, but find similar solutions. This suggests that for CNNs, the selection of the training set may be more important than the selection of the convolutional architecture. keyword: ResNet, Inception, CNN, Feature Evaluation, Feature Mapping.
DF2Net: A Dense-Fine-Finer Network for Detailed 3D Face Reconstruction

Reconstructing the detailed geometric structure from a single face image is a challenging problem due to its ill-posed nature and the fine 3D structures to be recovered. This paper proposes a deep Dense-Fine-Finer Network (DF2Net) to address this challenging problem. DF2Net decomposes the reconstruction process into three stages, each of which is processed by an elaborately-designed network, namely D-Net, F-Net, and Fr-Net. D-Net exploits a U-net architecture to map the input image to a dense depth image. F-Net refines the output of D-Net by integrating features from depth and RGB domains, whose output is further enhanced by Fr-Net with a novel multi-resolution hypercolumn architecture. In addition, we introduce three types of data to train these networks, including 3D model synthetic data, 2D image reconstructed data, and fine facial images. We elaborately exploit different datasets (or combination) together with well-designed losses to train different networks. Qualitative evaluation indicates that our DF2Net can effectively reconstruct subtle facial details such as small crow's feet and wrinkles. Our DF2Net achieves performance superior or comparable to state-of-the-art algorithms in qualitative and quantitative analyses on real-world images and the BU-3DFE dataset. Code and the collected 70K image-depth data will be publicly available.
Multifactorial optimization via explicit multipopulation evolutionary framework

Multifactorial Optimization (MFO) has attracted considerable attention in the community of evolutionary computation, which aims to deal with multiple optimization tasks simultaneously by information transfer. Unfortunately, information transfer may cause both positive and negative effects. To address this issue, this paper exploits an explicit multipopulation evolutionary framework (MPEF) to intelligently take advantage of positive information transfer and effectively reduce negative information transfer. In MPEF, each task possesses an independent population and has its random mating probability for exploiting the information of other tasks. Moreover, the random mating probability of each task is adjusted adaptively. The benefits of using MPEF are twofold. 1) Various well-developed search engines can be easily embedded into MPEF for solving the single task of multifactorial optimization problems. 2) The positive information transfer can be exploited. Meanwhile, negative information transfer can be prevented. A multifactorial evolutionary algorithm (named MFMP) is realized as an instance by embedding a well-designed search engine into MPEF. The experimental results on some MFO benchmark problems demonstrate the advantage of MFMP over some state-of-the-art algorithms. Moreover, MFMP is also successfully employed to solve the spread spectrum radar polyphase code design (SSRPCD) problem.
Robot acceptance model for care (RAM-care): A principled approach to the intention to use care robots

Robots are emerging in welfare services, and organizations require information on whether novel technologies are approved among staff. On the basis of technology acceptance theories, this study proposes a model that adds a principled approach to the intention to use care robots. Data of 544 professionals with care robot experience were collected. The use intention was predicted by usefulness, enjoyment, social influence, and attitude. Respondents who found robots useful and accepted by their colleagues were more likely to view robot use as consistent with their personal values. The care robot acceptance model supports consideration of the profession-specific context in robotization.
DeceptionNet: Network-Driven Domain Randomization

We present a novel approach to tackle domain adaptation between synthetic and real data. Instead, of employing "blind" domain randomization, i.e., augmenting synthetic renderings with random backgrounds or changing illumination and colorization, we leverage the task network as its own adversarial guide toward useful augmentations that maximize the uncertainty of the output. To this end, we design a min-max optimization scheme where a given task competes against a special deception network to minimize the task error subject to the specific constraints enforced by the deceiver. The deception network samples from a family of differentiable pixel-level perturbations and exploits the task architecture to find the most destructive augmentations. Unlike GAN-based approaches that require unlabeled data from the target domain, our method achieves robust mappings that scale well to multiple target distributions from source data alone. We apply our framework to the tasks of digit recognition on enhanced MNIST variants, classification and object pose estimation on the Cropped LineMOD dataset as well as semantic segmentation on the Cityscapes dataset and compare it to a number of domain adaptation approaches, thereby demonstrating similar results with superior generalization capabilities.
Centralized Core-granular Scheduling for Serverless Functions

In recent years, many applications have started using serverless computing platforms primarily due to the ease of deployment and cost efficiency they offer. However, the existing scheduling mechanisms of serverless platforms fall short in catering to the unique characteristics of such applications: burstiness, short and variable execution times, statelessness and use of a single core. Specifically, the existing mechanisms fall short in meeting the requirements generated due to the combined effect of these characteristics: scheduling at a scale of millions of function invocations per second while achieving predictable performance.
CosRec: 2D Convolutional Neural Networks for Sequential Recommendation

Sequential patterns play an important role in building modern recommender systems. To this end, several recommender systems have been built on top of Markov Chains and Recurrent Models (among others). Although these sequential models have proven successful at a range of tasks, they still struggle to uncover complex relationships nested in user purchase histories. In this paper, we argue that modeling pairwise relationships directly leads to an efficient representation of sequential features and captures complex item correlations. Specifically, we propose a 2D convolutional network for sequential recommendation (CosRec). It encodes a sequence of items into a three-way tensor; learns local features using 2D convolutional filters; and aggregates high-order interactions in a feedforward manner. Quantitative results on two public datasets show that our method outperforms both conventional methods and recent sequence-based approaches, achieving state-of-the-art performance on various evaluation metrics.
A Survey of Visual Analytics for Public Health

Abstract We describe visual analytics solutions aiming to support public health professionals, and thus, preventive measures. Prevention aims at advocating behaviour and policy changes likely to improve human health. Public health strives to limit the outbreak of acute diseases as well as the reduction of chronic diseases and injuries. For this purpose, data are collected to identify trends in human health, to derive hypotheses, e.g. related to risk factors, and to get insights in the data and the underlying phenomena. Most public health data have a temporal character. Moreover, the spatial character, e.g. spatial clustering of diseases, needs to be considered for decision‐making. Visual analytics techniques involve (subspace) clustering, interaction techniques to identify relevant subpopulations, e.g. being particularly vulnerable to diseases, imputation of missing values, visual queries as well as visualization and interaction techniques for spatio‐temporal data. We describe requirements, tasks and visual analytics techniques that are widely used in public health before going into detail with respect to applications. These include outbreak surveillance and epidemiology research, e.g. cancer epidemiology. We classify the solutions based on the visual analytics techniques employed. We also discuss gaps in the current state of the art and resulting research opportunities in a research agenda to advance visual analytics support in public health.
Automatic extrinsic calibration between a camera and a 3D Lidar using 3D point and plane correspondences

This paper proposes an automated method to obtain the extrinsic calibration parameters between a camera and a 3D lidar with as low as 16 beams. We use a checkerboard as a reference to obtain features of interest in both sensor frames. The calibration board centre point and normal vector are automatically extracted from the lidar point cloud by exploiting the geometry of the board. The corresponding features in the camera image are obtained from the camera's extrinsic matrix. We explain the reasons behind selecting these features, and why they are more robust compared to other approaches. To obtain the optimal extrinsic parameters, we choose a genetic algorithm to address the highly non-linear state space. The process is automated after defining the bounds of the 3D experimental region relative to the lidar, and the true board dimensions. In addition, the camera is assumed to be intrinsically calibrated. Our method requires a minimum of 3 checkerboard poses, and the calibration accuracy is demonstrated by evaluating our algorithm using real world and simulated features.