Reviewerly/exHarmony huggingface dataset:
Covered Face Recognition Based on Deep Convolution Generative Adversarial Networks

At present, the use of deep convolution generative adversarial networks to generate images is a new research direction in the field of machine vision. It can generate very high quality face pictures, so it plays a very important role in the field of criminal investigation. But during surveillance, suspects often wear face covers, such as glasses, masks, hats and so on. It may affect the machine's recognition of the face. The traditional method is to complete the incomplete image, that is, to imagine and complete the covered part, and then carry on the face recognition. In this paper, an image arithmetic operation based on DCGAN is proposed to process the traditional photos so and store them in the face database, so that the details of the human face can be well preserved. When face recognition is carried out, such pictures are used to assist in face recognition. The experimental results show that this method can improve the accuracy.
A New Sample-Efficient PAC Reinforcement Learning Algorithm

This paper introduces a new hybrid PAC RL algorithm for MDPS, which intelligently maintains favorable features of its parents. The DDQ algorithm, integrates model-free and model-based learning approaches, preserving some advantages from both. A PAC analysis of the DDQ algorithm is presented and its sample complexity is explicitly bounded. Numerical results from a small-scale example motivated by work on human-robot interaction models corroborates the theoretical predictions on sample complexity.
Planar polynomials arising from linearized polynomials

In this paper, we construct planar polynomials of the type [Formula: see text], with [Formula: see text]. In particular, we completely classify the pairs [Formula: see text] such that [Formula: see text] is planar using connections with algebraic curves over finite fields.
Dual Head Network for No-Reference Quality Assessment Towards Realistic Night-Time Images

No-reference image quality assessment (NR-IQA), which devotes to predicting image quality without relying on the corresponding pristine counterpart, develops rapidly in recent years. However, little investigation has been dedicated to quality assessment of realistic night-time images. Existing NR-IQA algorithms laboriously cope with this night-time scenario since complicated authentic distortions such as low contrast, blurred details, and reduced visibility usually appear on it. In this paper, we propose an end-to-end NR-IQA model to meet this challenge based on a multi-stream deep convolutional neural network (DCNN). Two streams, brightness-aware CNN and naturalness-aware CNN are constructed respectively by a brightness-altered image identification task with a self-established dataset and a quality-prediction regression task with an existing authentically-distorted IQA dataset to improve quality-aware initializations. In this case, given the quick convergence and little transformation in the lower layers, a shallow-layer-shared architecture is explored to reduce computational cost. Finally, the features of these two pipelines are collected by an effective pooling method and then concatenated as the image representation for fine-tuning. The effectiveness and efficiency of the proposed method are verified by several different experiments on the NNID, CCRIQ and LIVE Challenge databases. Furthermore, the superiority of wide applications such as for contrast-distorted and driving scenarios is demonstrated on the CID2013, CCID2014 and BBD-100k databases.
ANAC 2017: Repeated Multilateral Negotiation League

The Automated Negotiating Agents Competition (ANAC) is annually organized competition to facilitate the research on automated negotiation. This paper presents the ANAC 2017 Repeated Multilateral Negotiation League. As human negotiators do, agents are supposed to learn from their previous negotiations and improve their negotiation skills over time. Especially, when they negotiate with the same opponent on the same domain, they can adopt their negotiation strategy according to their past experiences. They can adjust their acceptance threshold or bidding strategy accordingly. In ANAC 2017, participants aimed to develop such a negotiating agent. Accordingly, this paper describes the competition settings and results with a brief description of the winner negotiation strategies.
A Comparative Study of the Influence of the Screen Size of Mobile Devices on the Experience Effect of 3D Content in the Form of AR/VR Technology

Differences in screen size of mobile devices can affect users' experience of products, but very few researches have confirmed that differences in screen size of mobile devices can affect users' experience of VR/AR content are few, or even affect the choice of VR/AR content form by 3D content creators. The authors use the AR and VR forms of the same 3D content to evaluate the participants' convenience, intuitive feedback and comfort of VR/AR interactive experience on mobile devices with different screen sizes, in order to explore whether the difference of screen size affects the experience effect of 3D content AR/VR. Research has shown that participants tend to use larger screen mobile devices for interactive experiences, but not the larger the screen of the mobile device used, the stronger the willingness of participants are. We found that participants tend to use mobile devices with moderate screen size for VR/AR interaction.
Convergence of Generalized Probability Mixtures That Describe Adaptive Fuzzy Rule-based Systems

A generalized probability mixture density governs the rule-base structure of an additive fuzzy system. A new theorem allows such a mixture to absorb any bounded real function by mixing two normal densities. We further show that a sequence of adaptive fuzzy systems defines a corresponding sequence of uniformly convergent generalized Gaussian mixtures. The result applies to any uniformly convergent sequence of function approximators. It gives a practical way to define approximating mixtures with adaptive fuzzy systems or neural networks. Users can combine any number of these rule-based systems by mixing their generalized mixtures.
Improving Automatic Speech Recognition by Classifying Adult and Child Speakers into Separate Groups using Speech Rate Rhythmicity Parameter

When children's speech is transcribed using acoustic models trained on adults' data, a severely degraded recognition performance is obtained. Similar degradations are noted on recognizing adults' speech using an automatic speech recognition (ASR) system trained on children's speech. This problem can be overcome by using two separate ASR systems for the two groups of speakers. But this approach requires an effective technique to detect whether the given data is from adult or child speaker. In this paper, we present a very simple and novel technique to do the same. The proposed approach is based on speechrate rhythmicity parameter (SRRP). Since the speaking-rates for adults and children differ significantly, the SRRP values are also very different for the two groups of speakers. Hence, by computing the SRRP value for a given speech utterance, it can be easily determined whether it is from adult or child speaker. The corresponding ASR systems can then be used to achieve improved recognition performance. Alternatively, existing techniques for improving children's speech recognition on adult data trained systems can be directly applied once it is known that the data is from a child speaker. Both these aspects have been experimentally validated in this work.
Paraconsistent Artificial Neural Network Applied to Agribusiness

Brazil is the world’s second largest exporter of soybeans, but growers face challenges, individually crop pests. Information Technology can provide solutions that contribute to the fight against brown stink bugs since this is the insect that causes the most loss of Brazilian production. The Paraconsistent Logic and the Paraconsistent Artificial Neural Networks allow us to create solutions for the identification of the insect in the plants, from in loco images. This article explored the techniques available for the construction of applications in Agriculture 4.0 for insect recognition, in order to facilitate the identification of them in the plantations, through the recognition of patterns of the aerial parts of the plant. The objective of this research is exploratory, with a qualitative approach and bibliographic and documentary procedures. Sources were used as scientific articles, research on international institutional sites, and research of data in national governmental institutions that work with the production, control, and export of this commodity. The biggest challenge of Agriculture 4.0 is to integrate technologies that can contribute to agribusiness. This is because farmers face difficulties accessing the internet in the field, which harms the use of devices in the plantations. This problem also affects countries in Europe, such as Germany, that need to ensure fiber-optic internet in rural areas. Despite the challenges encountered, solutions are using Artificial Intelligence in the area of agriculture that has contributed to the Brazilian agribusiness but is still an unexplored strand.
Deep Understanding of Breast Density Classification

We have developed a deep learning architecture, DualViewNet, for mammogram density classification as well as a novel metric for quantifying network preference of mediolateral oblique (MLO) versus craniocaudal (CC) views in density classification. Also, we have provided thorough analysis and visualization to better understand the behavior of deep neural networks in density classification. Our proposed architecture, DualViewNet, simultaneously examines and classifies both MLO and CC views corresponding to the same breast, and shows best performance with a macro average AUC of 0.8970 and macro average 95% confidence interval of 0.8239-0.9450 obtained via bootstrapping 1000 test sets. By leveraging DualViewNet we provide a novel algorithm and quantitative comparison of MLO versus CC views for classification and find that MLO provides stronger influence in 1,187 out of 1,323 breasts.
ZenDen - A Personalized House Searching Application

A method directly linking buyers and sellers in the housing market would benefit both parties in both the renting and purchasing domains. The real estate mobile applications available today, such as Zillow and Redfin, are highly dependent on filter based searches (location, price, bedrooms, etc) and contain many screens that require zooming in and out of map-based interfaces. Furthermore, the student market and lower-income demographics that are more likely renting a house instead of buying are usually not the focus of these applications. Described in this paper is a novel mobile application that will change the way people in the renting market, e.g., university students, find lodging. The implementation contains a streamlined swipe-based user interface backed by a user-house recommender system to manage content. Deep learning techniques are used in building the recommender system that recommends houses to users based on their view history. For image classification, we build convolutional neural networks (CNN) for analyzing house images. The goal is to create a personalized, easy to use application that will reduce the effort and time required for people in the renting market to find housing.
Digital cytology: Look how much has been achieved

CytopathologyVolume 31, Issue 5 p. 370-371 EDITORIAL Digital cytology: Look how much has been achieved Liron Pantanowitz, Corresponding Author Liron Pantanowitz lironp@med.umich.edu Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA Correspondence Liron Pantanowitz, Department of Pathology & Clinical Labs, University of Michigan, NCRC Bldg 35, Room 36-1221-35 2800 Plymouth Road, Ann Arbor, MI 48109-2800, USA. Email: lironp@med.umich.eduSearch for more papers by this author Liron Pantanowitz, Corresponding Author Liron Pantanowitz lironp@med.umich.edu Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA Correspondence Liron Pantanowitz, Department of Pathology & Clinical Labs, University of Michigan, NCRC Bldg 35, Room 36-1221-35 2800 Plymouth Road, Ann Arbor, MI 48109-2800, USA. Email: lironp@med.umich.eduSearch for more papers by this author First published: 28 August 2020 https://doi.org/10.1111/cyt.12866Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume31, Issue5Special Issue: Digital imaging in cytologySeptember 2020Pages 370-371 RelatedInformation
Fuel for Cyclones: Quantification of Ocean‐Atmosphere Energy Exchange during Tropical Cyclones in the Bay of Bengal Using Indian Ocean Moored Observatories

Abstract Based on the in-situ subsurface thermal and salinity measurements from the Ocean Moored Buoy Network for Northern Indian Ocean (OMNI) during the passage of very severe tropical cyclones (TCs) in the Bay of Bengal, we have identified that the depth of ocean‐atmosphere interaction is limited by the depth of the pycnocline. During the TC Vardha and Phailin with cyclone-period-averaged wind speeds of 8 and 21 m/s, respectively, the maximum possible rates of water-vapor generation during the cyclone period, computed based on the salinity changes and considering precipitation, are 1.0 and 9.3 kg/m 2 /h, respectively. For the same wind speeds, based on the ocean heat content (OHC) changes, it is quantified that ~78% and 89% of the OHC changes are in the form of latent heat. The real-time availability of the in-situ subsurface parameters can be used in the ocean-atmosphere coupled models and intensification studies.
An enhanced adaptive global‐best harmony search algorithm for continuous optimization problems

Abstract This paper presents an enhanced adaptive global‐best harmony search (EAGHS) to solve global continuous optimization problems. The global‐best HS (GHS) is one of the strongest versions of the classical HS algorithm that hybridizes the concepts of swarm intelligence and conventional HS. However, randomized selection of harmony in the permissible interval diverts the GHS algorithm from the global optimum. To address this issue, the proposed EAGHS method introduces a dynamic coefficient into the GHS algorithm to increase the search power in early iterations. Various complex and extensively‐applied benchmark functions are used to validate the developed EAGHS algorithm. The results indicate that the EAGHS algorithm offers faster convergence and better accuracy than the standard HS, GHS and other similar algorithms. Further analysis is performed to evaluate the sensitivity of the proposed method to the changes of parameters such as harmony memory consideration rate, harmony search memory, and larger dimensions.
Real-Time Globally Consistent Dense 3D Reconstruction With Online Texturing

High-quality reconstruction of 3D geometry and texture plays a vital role in providing immersive perception of the real world. Additionally, online computation enables the practical usage of 3D reconstruction for interaction. We present an RGBD-based globally-consistent dense 3D reconstruction approach, where high-quality (i.e., the spatial resolution of the RGB image) texture patches are mapped on high-resolution ([Formula: see text]) geometric models online. The whole pipeline uses merely the CPU computing of a portable device. For real-time geometric reconstruction with online texturing, we propose to solve the texture optimization problem with a simplified incremental MRF solver in the context of geometric reconstruction pipeline using sparse voxel sampling strategy. An efficient reference-based color adjustment scheme is also proposed to achieve consistent texture patch colors under inconsistent luminance situations. Quantitative and qualitative experiments demonstrate that our online scheme achieves a realistic visualization of the environment with more abundant details, while taking fairly compact memory consumption and much lower computational complexity than existing solutions.
Design of Wheelchair based on Electrooculography

The number of disable people are increasing at a rapid rate, disability to control limbs due to accidents or inherently are major cause of immobility. Immobility hinders daily activities and leads to social restrictions which isolate them from the rest. Various techniques like mechanized wheelchair have been designed for quadriplegic people. In this paper we have proposed a prototype design of an automated wheelchair using electrooculography (EOG) signal. The performance of the proposed prototype is cost effective since EOG signal acquisition is done using Arduino Uno and Leonardo. Eye movement can be used to monitor various simple tasks by the physically challenged persons. This paper describes the acquisition and analysis of EOG signals using Arduino resulting in movement of the prototype in the forward and backward direction. This proposed design here uses few electrodes for signal acquisition which reduces the chance of occurrence of artifacts and interference, also a simple circuit design for signal acquisition using Arduino makes it cost effective for the patients who cannot afford costly automated wheelchairs. The standing potential difference between the eyes can be estimated by measuring the voltage induced by external battery across a system of electrodes placed around the eyes as the eye-gaze changes, thus obtaining the EOG. This proposed design can be used as an assistive device for monitoring and performing various small activities by paralyzed people with ease. It is a cost effective and simple design.
Evaluation of Gradient Descent Optimization method for SAR Images Co-registration

Registration or matching process aims to find the misalignment between two or more images concerning the same area to detect the values of the mapping matrix in order to transform interest points in one image to its correspondence in the others. This paper presents a dynamic approach aiming to improve the performance of the registration process for synthetic aperture radar (SAR) images. First, the noise resulting from the capturing process is reduced by using a smoothing filter based on kernel-gaussian to reduce the amplification of noise. Then; a combination of two area- based matching (ABM) methods is used. The first method is carried out using Crosscorrelation approach, acting as coarse registration step. The second method is achieved by using regular step gradient descent (RSGD) optimizer, acting as fine registration step. Evaluation of the performance concerning the proposed manner is achieved by comparing to the state-of-the art detectors as Harris, Shi-Tomasi, and Features from Accelerated Segment Test (FAST) detectors. Metric factors to achieve the comparison are mean square error (MSE) and peak signal-to-noise ratio (PSNR) between the input images. Results demonstrate a highly performance for the proposed method compared to the others where it has a high robustness and minimizes the noise of the input image.
Transition to the Intelligent Services Ecosystem: Integration of Block Chain and Internet of Things in Supply Chain Management

The traditional supply chain management is facing challenges of low transaction efficiency, high cost and insecurity among multi-participants, which is urgent to accelerate the evolution of traditional supply chain to a connected, intelligent and efficient supply chain ecosystem. Previous literatures have discussed the application of the Internet of Things in supply chain, but there are insufficient studies on how to optimize the supply chain management efficiency with the integration of the Internet of Things and block chains. Therefore, this paper analyses the current situation and development trend of the Internet of Things and block chain. Furthermore, based on the conditions of multi-agents of supply chain, this paper also studies the advantages and mechanisms of the integration of Internet of Things and block chain in promoting the supply chain management efficiency, as well as the concomitant difficulties and bottlenecks. Finally, this paper puts forward some implications on how to optimize the integration of the Internet of Things and the block chain in the intelligent services ecosystem of supply chain management.
Exchanging Expertise across Cultures and Time: Participatory Design Approaches for Creating Community Museums

This paper presents a case study where participatory design practices were used to design a cultural museum and inaugural exhibition with designers from an Australian university and the Kelabit community in the Highlands of Borneo. We draw on experiences from fieldtrips, across a five-year period, investigating the exchange of expertise between participants. The findings show a shift in roles when sharing expertise throughout our participatory design process. Participants experienced a lack of clarity and a sense of discomfort while participating in the design of the museum resulting in the need to continually negotiate leadership of the project. The designers discovered their role was to facilitate intangible conversations into tangible outcomes, where participatory design tools reshaped conversations rather than designed final artefacts. We argue that when exchanging expertise across cultures in participatory design, participant roles become organic and ambiguous.
Reversible ECG Data Hiding: Analysis and Comparison of ANN, Regression SVM and Random Forest Regression

The telemedicine industry is rapidly shaping the medical facilities; it starts from a basic task like booking appointments and goes up to complex tasks like diagnosis and surgery. The telemedicine industry is flourishing and a huge amount of patient data is flowing over internet. In such scenario it is very important to ensure integrity and safety of medical data. In this paper a reversible data hiding technique for ECG (electrocardiogram) signals is discussed and performance analysis of random forest (RF) regression, regression SVM (support vector machine) and artificial neural network (ANN) is done. RF, SVM, and ANN are used to predict the ECG samples, and watermark is embedded using prediction error expansion (PEE). The performances of all the models are measured using SNR (signal to noise ratio), (PRD) (percentage residual difference), & NCC (normalized cross-correlation). Models are tested for different embedding strength, ANN model has superior performance over SVM and RF. Due to reversible nature of the scheme, original signal can be completely recovered from watermarked signal.
Measuring the Business Value of Infrastructure Migration to the Cloud

Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) adoption typically require radical changes in an organisation’s IT operations and have widespread implications that go beyond simple cost savings. This chapter presents a practical framework for estimating the Return on Investment (ROI) for IaaS and PaaS from the customer perspective. The proposed framework aims to overcome the main limitations of commonly-used Total Cost of Ownership (TCO) calculators by including both tangible and intangible costs and benefits to provide a more comprehensive ROI estimation. The application of the framework is illustrated using a real-life case study of infrastructure migration.