Reviewerly/exHarmony huggingface dataset:
Robust Visual Saliency Optimization Based on Bidirectional Markov Chains

Saliency detection aims to automatically highlight the most important area in an image. Traditional saliency detection methods based on absorbing Markov chain only take into account boundary nodes and often lead to incorrect saliency detection when the boundaries have salient objects. In order to address this limitation and enhance saliency detection performance, this paper proposes a novel task-independent saliency detection method based on the bidirectional absorbing Markov chains that jointly exploits not only the boundary information but also the foreground prior and background prior cues. More specifically, the input image is first segmented into number of superpixels, and the four boundary nodes (duplicated as virtual nodes) are selected. Subsequently, the absorption time upon transition node’s random walk to the absorbing state is calculated to obtain the foreground possibility. Simultaneously, foreground prior (as the virtual absorbing nodes) is used to calculate the absorption time and get the background possibility. In addition, the two aforementioned results are fused to form a combined saliency map which is further optimized by using a cost function. Finally, the superpixel-level saliency results are optimized by a regularized random walks ranking model at multi-scale. The comparative experimental results on four benchmark datasets reveal superior performance of our proposed method over state-of-the-art methods reported in the literature. The experiments show that the proposed method is efficient and can be applicable to the bottom-up image saliency detection and other visual processing tasks.
ICONATE: Automatic Compound Icon Generation and Ideation

Compound icons are prevalent on signs, webpages, and infographics, effectively conveying complex and abstract concepts, such as "no smoking" and "health insurance", with simple graphical representations. However, designing such icons requires experience and creativity, in order to efficiently navigate the semantics, space, and style features of icons. In this paper, we aim to automate the process of generating icons given compound concepts, to facilitate rapid compound icon creation and ideation. Informed by ethnographic interviews with professional icon designers, we have developed ICONATE, a novel system that automatically generates compound icons based on textual queries and allows users to explore and customize the generated icons. At the core of ICONATE is a computational pipeline that automatically finds commonly used icons for sub-concepts and arranges them according to inferred conventions. To enable the pipeline, we collected a new dataset, Compicon1k, consisting of 1000 compound icons annotated with semantic labels (i.e., concepts). Through user studies, we have demonstrated that our tool is able to automate or accelerate the compound icon design process for both novices and professionals.
Ensuring user authentication and data integrity in multi-cloud environment

Abstract The necessity to improve security in a multi-cloud environment has become very urgent in recent years. Although in this topic, many methods using the message authentication code had been realized but, the results of these methods are unsatisfactory and heavy to apply, which, is why the security problem remains unresolved in this environment. This article proposes a new model that provides authentication and data integrity in a distributed and interoperable environment. For that in this paper, the authors first analyze some security models used in a large and distributed environment, and then, we introduce a new model to solve security issues in this environment. Our approach consists of three steps, the first step, was to propose a private virtual network to secure the data in transit. Secondly, we used an authentication method based on data encryption, to protect the identity of the user and his data, and finally, we realize an algorithm to know the integrity of data distributed on the various clouds of the system. The model achieves both identity authentication and the ability to inter-operate between processes running on different cloud’s provider. A data integrity algorithm will be demonstrated. The results of this proposed model can efficiently and safely construct a reliable and stable system in the cross-cloud environment.
Assessing 2D and 3D Heatmaps for Comparative Analysis

Heatmaps are a popular visualization technique that encode 2D density distributions using color or brightness. Experimental studies have shown though that both of these visual variables are inaccurate when reading and comparing numeric data values. A potential remedy might be to use 3D heatmaps by introducing height as a third dimension to encode the data. Encoding abstract data in 3D, however, poses many problems, too. To better understand this tradeoff, we conducted an empirical study (N=48) to evaluate the user performance of 2D and 3D heatmaps for comparative analysis tasks. We test our conditions on a conventional 2D screen, but also in a virtual reality environment to allow for real stereoscopic vision. Our main results show that 3D heatmaps are superior in terms of error rate when reading and comparing single data items. However, for overview tasks, the well-established 2D heatmap performs better.
Deep Open-Set Domain Adaptation for Cross-Scene Classification based on Adversarial Learning and Pareto Ranking

Most of the existing domain adaptation (DA) methods proposed in the context of remote sensing imagery assume the presence of the same land-cover classes in the source and target domains. Yet, this assumption is not always realistic in practice as the target domain may contain additional classes unknown to the source leading to the so-called open set DA. Under this challenging setting, the problem turns to reducing the distribution discrepancy between the shared classes in both domains besides the detection of the unknown class samples in the target domain. To deal with the openset problem, we propose an approach based on adversarial learning and pareto-based ranking. In particular, the method leverages the distribution discrepancy between the source and target domains using min-max entropy optimization. During the alignment process, it identifies candidate samples of the unknown class from the target domain through a pareto-based ranking scheme that uses ambiguity criteria based on entropy and the distance to source class prototype. Promising results using two cross-domain datasets that consist of very high resolution and extremely high resolution images, show the effectiveness of the proposed method.
Guess the Data: Data Work to Understand How People Make Sense of and Use Simple Sensor Data from Homes

Simple smart home sensors, e.g. for temperature or light, increasingly collect seemingly inconspicuous data. Prior work has shown that human sensemaking of such sensor data can reveal domestic activities. Such sensemaking presents an opportunity to empower people to understand the implications of simple smart home sensors. To investigate, we developed and field-tested the Guess the Data method, which enabled people to use and make sense of live data from their homes and to collectively interpret and reflect on anonymized data from the homes in our study. Our findings show how participants reconstruct behavior, both individually and collectively, expose the sensitive personal data of others, and use sensor data as evidence and for lateral surveillance within the household. We discuss the potential of our method as a participatory HCI method for investigating design of the IoT and implications created by doing data work on home sensors.
Single-stream long-term optical flow convolution network for action recognition of lameness dairy cow

Typical dairy cow lameness actions are only several seconds in duration and exhibit characteristic spatiotemporal structures. Manual lameness detection poses several problems, such as difficulty in the detection of sudden, severe or early lameness behavior. In this paper, our proposed method attempts to capture this structure and to learn action representations with convolutional neural networks. However, such representations are typically learned at the level of a few video frames, thereby failing to model actions over their full temporal extent. In this study, we learn video representations using neural networks with single-stream long-term optical flow convolution. To evaluate the performance of the algorithm, 756 videos were randomly selected from 1080 dairy cow videos as the original training videos, and the remaining videos were used as test videos. The experimental results demonstrate that single-stream long-term optical flow convolution network models with increased temporal extents improve the accuracy of dairy cow lameness action recognition. We also study the impacts of various low-level representations, such as raw values of video pixels and optical flow vector fields, and we demonstrate the importance of high-quality optical flow estimation for learning accurate dairy cow lameness action models. We report state-of-the-art results on challenging benchmarks for the dairy cow lameness action video set (98.24%).
Recognizing Named Entities in Agriculture Documents using LDA based Topic Modelling Techniques

Named Entity Recognition (NER) is one of the fundamental process in Natural Language Processing applications. In this paper, we propose an Agriculture Named Entity Recognition using Topic Modelling techniques (AERTM Algorithm). In the agriculture domain, we have identified Names of Crops, Soil Types, Names of Pathogen, Crop Diseases and Fertilizers as the key entities. Our work presents a hybrid approach using the agriculture vocabulary AGROVOC and the AERTM algorithm. We used AGROVOC for identifying crop names. But it failed to identify Soil Types, Crop Diseases and Fertilizers. Hence, for those entities we propose a Latent Dirichlet Allocation (LDA) based topic modelling algorithm. These named entities can be used for creating a knowledge base which can be further used mainly in Relation Extraction systems, forums supported by various Government distinguished repositories, etc. Because of the absence of benchmark agriculture data, we tested our model using 3000 sentences extracted from reputed agriculture sites. Human evaluation of the method confirms that our approach gives an accuracy of 80%.
SonicPrint

The advent of smart devices has caused unprecedented security and privacy concerns to its users. Although the fingerprint technology is a go-to biometric solution in high-impact applications (e.g., smart-phone security, monetary transactions and international-border verification), the existing fingerprint scanners are vulnerable to spoofing attacks via fake-finger and cannot be employed across smart devices (e.g., wearables) due to hardware constraints. We propose SonicPrint that extends fingerprint identification beyond smartphones to any smart device without the need for traditional fingerprint scanners. SonicPrint builds on the fingerprint-induced sonic effect (FiSe) caused by a user swiping his fingertip on smart devices and the resulting property, i.e., different users' fingerprint would result in distinct FiSe. As the first exploratory study, extensive experiments verify the above property with 31 participants over four different swipe actions on five different types of smart devices with even partial fingerprints. SonicPrint achieves up to a 98% identification accuracy on smartphone and an equal-error-rate (EER) less than 3% for smartwatch and headphones. We also examine and demonstrate the resilience of SonicPrint against fingerprint phantoms and replay attacks. A key advantage of SonicPrint is that it leverages the already existing microphones in smart devices, requiring no hardware modifications. Compared with other biometrics including physiological patterns and passive sensing, SonicPrint is a low-cost, privacy-oriented and secure approach to identify users across smart devices of unique form-factors.
A secured data hiding using affine transformation in video steganography

Network security is the most essential aspect of information technology among today’s emerging digital technologies which demands secured communication of information. In this digital network, it is essential to secure the data from intruders and unauthorized receivers. Steganography plays a vital role in secure transmission of data. This paper proposes a steganography method to hide data using affine transformation technique. The secret data are embedded in the coefficients of integer wavelet transform of the video frames. While embedding, the pixel values are distributed using affine transformation. The proposed method has been tested on many input data and the performance is evaluated both quantitatively and qualitatively. The results indicate the enhanced capability of the proposed method that can ensure imperceptible distortions with minimum computational cost in terms of PSNR factor over the existing methods.
Guidance and Control Law Design for a Slung Payload in Autonomous Landing: A Drone Delivery Case Study

This article presents a novel guidance and control design for a parcel tethered to a drone in its delivery mission. The proposed proportional-navigation-based guidance, integrated with custom-built control, enables to achieve autonomous payload landing. The novelty of the proposed method lies in two aspects: 1) the guidance law allows for soft landing and 2) the path-following control ensures the swing-free payload transportation that sets the solid foundation for landing. The development is verified by extensive simulations and further demonstrated by flight experiments.
Assessment of rooftop solar power technical potential in Hanoi city, Vietnam

Vietnam has great solar energy potential, in which photovoltaic (PV) power technology is developing rapidly in Vietnam and the investors are very interested in constructing the PV power station. Building the rooftop PV power stations can save monthly electricity costs for the owners and can sell the excess electricity from the PV power station to the power grid to get the economic benefits. Solar radiation in the Northern area in general and in Hanoi city, in particular, is lower than other provinces and cities in the Central area and the Southern area in Vietnam. The development of the rooftop solar power business in Hanoi is also affected because there is no specific assessment of the potential of this kind of solar energy. In this paper, the technical potential of rooftop solar power in Hanoi city is evaluated by using high-resolution remote sensing images technology, it can be seen that the total annual installed capacity of rooftop PV power and the PV optimal in Hanoi city are about 13,169.72 MWp and 37,591,481.20 MWh/year, respectively. Otherwise, the annual operation data of a rooftop PV power station in Hanoi is also collected to evaluate the efficiency in the real working condition, it is found that the payback in the case of actual operations is about 1 year longer than the theoretical calculation.
Autoencoders Based Deep Learner for Image Denoising

Nowadays, digital images have a valuable role in our daily life, and can be used for various of applications like fingerprint recognition, video surveillance etc. Sometimes, images get infected with noise due to many reasons such as defects in camera sensors, transmission in noisy channel, faulty memory locations in the hardware etc. Processing a noisy image is not advisable because usually it yields erroneous outcomes. So, as to improve it for subsequence processing, the noise must be eliminated from the image in advance. Therefore, there is a need of an efficient image denoising technique that helps to deal with noisy image. Image de-noising is a process to realign the original image from the degraded image. In this paper, autoencoders based deep learning model is proposed for image denoising. The autoencoders learns noise from the training images and then try to eliminate the noise for novel image. The experimental outcomes prove that this proposed model for PSNR has achieved higher result compared to the conventional models.
Hybrid Refinement-Correction Heatmaps for Human Pose Estimation

In this paper, we present a method (Hybrid-Pose) to improve human pose estimation in images. We adopt Stacked Hourglass Networks to design two convolutional neural network models, RNet for pose refinement and CNet for pose correction. The CNet (Correction Network) guides the pose refinement RNet (Refinement Network) to correct the joint location before generating the final pose. Each of the two models is composed of four hourglasses, and each hourglass generates a group of detection heatmaps for the joints. The RNet model hourglasses have the same structure. However, the CNet model is designed with hourglasses of different structures for pose guidance. Since the pose estimation in RGB images is very sensitive to the image scene, our proposed approach generates multiple outputs of detection heatmaps to broaden the searching scope for the correct joints locations. We use the RNet model to refine the joints locations in each hourglass stage horizontally, then the heatmaps of each stage are fused with the heatmaps of all the CNet model hourglasses vertically in a hybrid manner. Our method shows competitive results with the existing state-of-the-art approaches on MPII and FLIC benchmark datasets. Although our proposed method focuses on improving single-person pose estimation, we also show the influence of this improvement on multi-person pose estimation by detecting multiple people using SSD detector, then estimating the pose of each person individually.
A comprehensive database of Nature-Inspired Algorithms

These data contain a comprehensive collection of all Nature-Inspired Algorithms. This collection is a result of two corresponding surveys, where all Nature-Inspired Algorithms that have been published to-date were gathered and preliminary data acquired. The rapidly increasing number of nature-inspired approaches makes it hard for interested researchers to keep up. Moreover, a proper taxonomy is necessary, based on specific features of the algorithms. Different taxonomies and useful insight into the application areas that the algorithms have coped with is given through these data. This article provides a detailed description of the above mentioned collection.
Deep Adaptive LiDAR: End-to-end Optimization of Sampling and Depth Completion at Low Sampling Rates

Current LiDAR systems are limited in their ability to capture dense 3D point clouds. To overcome this challenge, deep learning-based depth completion algorithms have been developed to inpaint missing depth guided by an RGB image. However, these methods fail for low sampling rates. Here, we propose an adaptive sampling scheme for LiDAR systems that demonstrates state-of-the-art performance for depth completion at low sampling rates. Our system is fully differentiable, allowing the sparse depth sampling and the depth inpainting components to be trained end-to-end with an upstream task.
Gujarati Handwritten Character Recognition from Text Images

Today is the era of paperless office and governance. It comes with numerous advantages like increased productivity and efficiency, pervasiveness, storage optimization, robustness and eco-friendliness. Hence there is a need of converting paper documents into machine editable form. This leads to development of OCR (Optical Character Recognition). OCR is a technique to convert, mechanically or electronically an image, photo or scanned document of a handwritten text (HCR-Handwritten Character Recognition) or printed text (PCR- Printed Character Recognition) into digital text. HCR is a form of OCR that is specifically designed to recognize the handwritten text whereas PCR focuses on recognition of printed text. HCR is more challenging as compared to PCR due to diversity in human writing styles, size, curve, strokes and thickness of characters. Based on data acquisition mode, the OCR can either be online or offline. Offline recognition is performed in two ways: handwritten and printed [1]. In offline mode, the characters are on paper and captured using scanner or high-resolution camera whereas in online mode the pixel values of characters are captured by movement of cursor, pen or stylus. The HCR systems are readily available for foreign languages and many of the Indian languages like Bangla, Devanagari and Gurumukhi but for Gujarati language the HCR development is still in its infancy stage. This study focuses on development of an artificial intelligence based offline HCR system for Gujarati language. Important contribution of this study is data collection, of size 10,000 images from 250 number of people, of different age groups, of different professions. This paper describes a supervised classifier approach based on CNN (Convolutional Neural Networks) and MLP (Multi-Layer Perceptron) for recognition of handwritten Gujarati characters. A success rate of 97.21% is obtained using CNN and 64.48% using MLP. Lot of work has been done at character level, but very few has been done at word level recognition. Major focus of this study was on creating a continuous workflow for image to text conversion at word level.
Specification Patterns of Service-Based Applications Using Blockchain Technology

With the fast development of information technologies, traditional value-added chain business models are shifting to service-based applications (SBAs) to cope with ever-changing user behaviors and the modern social system. An SBA allows an organization to utilize external and distributed resources to achieve its business goals, especially when the Internet of Thing (IoT) will soon become mainstream. However, the development of SBAs is at its early stage with a number of unsolved issues, such as the availability of effective methods for services selection and composition, semantic representation of specifications, and security assurances. This article aims to address two main issues in SBAs: 1) the standardization of formulation and 2) the security assurance. A systematic method is proposed to formulate the specifications of services, the prevalent blockchain technology (BCT) is adopted to enable SBAs, and the proposed specification patterns and BCT are integrated as a BCT-based quality-of-service (QoS) framework to support service selections and workflow compositions.
A Framework for Sustainable Contact Tracing and Exposure Investigation for Large Health Systems

<h2>Abstract</h2> Contact tracing is a cornerstone of communicable disease containment and involves identifying, quarantining, and monitoring contacts of infected people. Although contact tracing is a known evidence-based strategy in the community setting, the COVID-19 pandemic highlighted the challenges to implementing labor-intensive contact tracing in the occupational setting of large health care systems and hospitals, the epicenter of the pandemic. We present a framework for feasible, scalable COVID-19 contact tracing in a large multistate health system in the United States employing approximately 69,000 health care personnel. The framework is shared with sufficient details to allow adoption or adaptation by other health systems. Continuous enhancement, optimization, and evaluation of the framework are ongoing.