Reviewerly/exHarmony huggingface dataset:
Intention Oriented Image Captions With Guiding Objects

Although existing image caption models can produce promising results using recurrent neural networks (RNNs), it is difficult to guarantee that an object we care about is contained in generated descriptions, for example in the case that the object is inconspicuous in the image. Problems become even harder when these objects did not appear in training stage. In this paper, we propose a novel approach for generating image captions with guiding objects (CGO). The CGO constrains the model to involve a human-concerned object when the object is in the image. CGO ensures that the object is in the generated description while maintaining fluency. Instead of generating the sequence from left to right, we start the description with a selected object and generate other parts of the sequence based on this object. To achieve this, we design a novel framework combining two LSTMs in opposite directions. We demonstrate the characteristics of our method on MSCOCO where we generate descriptions for each detected object in the images. With CGO, we can extend the ability of description to the objects being neglected in image caption labels and provide a set of more comprehensive and diverse descriptions for an image. CGO shows advantages when applied to the task of describing novel objects. We show experimental results on both MSCOCO and ImageNet datasets. Evaluations show that our method outperforms the state-of-the-art models in the task with average F1 75.8, leading to better descriptions in terms of both content accuracy and fluency.
Natural-based underwater image color enhancement through fusion of swarm-intelligence algorithm

Underwater imagery suffers from severe effects due to selective attenuation and scattering effects when light travels through water medium. Such damages limit the ability of vision tasks and reduce image quality. There are a lot of enhancement methods to improve the quality of underwater image. However, most of the methods produce distortion effects in the output images. The proposed natural-based underwater image color enhancement (NUCE) method consists of four steps. The first step introduces a new approach to neutralize underwater color cast. The inferior color channels are enhanced based on gain factors, which are calculated by considering the differences between the superior and inferior color channels. In the second step, the dual-intensity images fusion based on average of mean and median values is proposed to produce lower-stretched and upper-stretched histograms. The composition between these histograms improves the image contrast significantly. Next, the swarm-intelligence based mean equalization is proposed to improve the naturalness of the output image. Through the fusion of swarm intelligence algorithm, the mean values of inferior color channels are adjusted to be closed to the mean value of superior color channel. Lastly, the unsharp masking technique is applied to sharpen the overall image. Experiments on underwater images that are captured under various conditions indicate that the proposed NUCE method produces better output image quality, while significantly overcoming other state-of-the-art methods.
Self-Supervised deep homography estimation with invertibility constraints

Remarkable performance of the homography estimation has been achieved by the deep CNN based approaches. These homography estimation methods, more often than not, are supervised methods and rely too much on the ground truth annotations as they aim to learn the mapping between image pairs and homography. On the other hand, the inherent invertibility of homography is helpful to avoid over-fitting and improve the performance, which however is ignored by previous homography estimation methods. In this paper, we propose a novel homography estimation approach, named “Self-Supervised Regression Network(SSR-Net)”, which relaxes the need of ground truth annotations and takes advantage of invertibility constraints. We utilize spatial pyramid pooling modules to improve the quality of extracted features in each image by exploiting context information. To employ the invertibility constraints, we adopt the matrix representation of the homography rather than the commonly used 4-point parameterization in other methods. Our proposed SSR-Net produce homography matrices and synthetic images in a cycled way. The network are trained in a self-supervised way by minimizing the combination of photometric loss and invertibility loss. Experiments on the synthetic dataset generated from MSCOCO dataset show that our proposed method outperforms several state-of-the-art approaches.
Standard of reference in operational day-ahead deterministic solar forecasting

Skill scores can be used to compare deterministic (also known as single-valued or point) forecasts made using different models at different locations and time periods. To compute the skill score, a reference forecasting method is needed. Nonetheless, there is no consensus on the choice of reference method. In this paper, three classes of commonly used references methods, namely, climatology, persistence, and their linear combination, are studied in a day-ahead solar forecasting scenario. Day-ahead global solar irradiance forecasts with an hourly resolution are generated using research-grade data from 32 sites around the globe, over a period of 1 year, in an operational manner. To avoid exaggerating the skill scores, it is generally agreed that the most accurate naïve forecasting method should be chosen as the standard of reference. In this regard, the optimal convex combination of climatology and persistence is highly recommended to be used as the standard of reference for day-ahead solar forecasting.
Quality-Aware Unpaired Image-to-Image Translation

Generative adversarial networks (GANs) have been widely used for the image-to-image translation task. While these models rely heavily on the labeled image pairs, recently some GAN variants have been proposed to tackle the unpaired image translation task. These models exploited supervision at the domain level with a reconstruction process for unpaired image translation. On the other hand, parallel works have shown that leveraging perceptual loss functions based on high-level deep features could enhance the generated image quality. Nevertheless, as these GAN-based models either depended on the pretrained deep network structure or relied on the labeled image pairs, they could not be directly applied to the unpaired image translation task. Moreover, despite the improvement of the introduced perceptual losses from deep neural networks, few researchers have explored the possibility of improving the generated image quality from classical image quality measures. To tackle the above two challenges, in this paper, we propose a unified quality-aware GAN-based framework for unpaired image-to-image translation, where a quality-aware loss is explicitly incorporated by comparing each source image and the reconstructed image at the domain level. Specifically, we design two detailed implementations of the quality loss. The first method is based on a classical image quality assessment measure by defining a classical quality-aware loss to ensure similar quality score between an original image and the reconstructed image at the domain level. The second method proposes an adaptive deep network based loss that compares the high level content structure between each original image and its reconstructed image from the generator. Finally, extensive experimental results on many real-world datasets clearly show the quality improvement of our proposed framework, and the superiority of leveraging classical image quality measures for unpaired image translation compared to the deep network based model.
Blockchain: Trends and future opportunities

Blockchain has been receiving considerable attention from the research community lately. Several research efforts have proposed the use of blockchain in many application domains. Not all of these application areas have received the same level of blockchain attention however and, for some of the applications, blockchain investigation has even stopped. Blockchain has the ability to shape the future, but not in all applications and topics. We explored those application areas that have not received much blockchain attention so that we can focus more on the application domains that have strong potential to reach the customer and have a high impact on the market. Based on our quantitative evaluation of blockchain applications and topics, we recommend those that need further investigation and implementation.
Critical Success Factors for Implementing Artificial Intelligence (AI) Projects in Dubai Government United Arab Emirates (UAE) Health Sector: Applying the Extended Technology Acceptance Model (TAM)

Recently, the government of United Arab of Emirates (UAE) is focusing on Artificial Intelligence (AI) strategy for future projects that will serve various sectors. Health care sector is one of the significant sectors they are focusing on and the planned (AI) projects of it is aiming to minimize chronic and early prediction of dangerous diseases affecting human beings. Nevertheless, project success depends on the adoption and acceptance by the physicians, nurses, decision makers and patients. The main purpose of this paper is to explore out the critical success factors assist in implementing artificial intelligence projects in the health sector. Besides, the founded gap for this topic was explored as there is no enough sharing of multiple success factors that assist in implementing artificial intelligence projects in the health sector precisely. A modified proposed model for this research was developed by using the extended TAM model and the most widely used factors. Data of this study was collected through survey from employees working in the health and IT sectors in UAE and total number of participants is 53 employees. The outcome of this questionnaire illustrated that managerial, organizational, operational and IT infrastructure factors have a positive impact on (AI) projects perceived ease of use and perceived usefulness.
An Implementation of Smart Contracts by Integrating BIM and Blockchain

This paper presents an implementation of smart contracts by integrating BIM and blockchain. After briefly reviewing the literature regarding blockchain technology, smart contracts, and integration of the BIM and blockchain, a blockchain network using Hyperledger fabric is proposed and tested to govern a sample construction project. The proposed framework does not utilize the cryptocurrency aspect of the blockchain as the payment form. Instead, it discusses the integration of the current banking system and the use of fiat currencies in transactions. The results show that blockchain is a viable system for governing construction project contracts by automating the consequences of each transaction and maintaining a tamper proof record of project progress, which would be valuable in any kind of dispute resolution. The blockchain network developed in this study implements the smart contract as its network operation logic. As a result, the notion of having to translate all the traditional contract clauses to the computer program is shown to be unnecessary and to some extent not suitable for construction, due to the complexity, fluidity, and high uncertainties involved in each project.
Breast Cancer Prediction via Machine Learning

Breast cancer is one of the most common and leading causes of cancer among women. Currently, it has become the common health issue, and its incidence has increased recently. Prior identification is the best way to manage breast cancer results. Computer-aided detection or diagnosis (CAD) systems plays a major role in prior identification of breast cancer and can be used for reduction of death rate among women. The main intention of this paper is to make use of the recent advances in the development of CAD systems and related techniques. The mainstay of the project is to predict whether the person is having breast cancer or not. Machine learning is nothing but training the machines to learn and perform by itself without any explicit program or instruction. So here, predicting whether a person is suffering with breast cancer or not is done with the help of the trained data.
Direct Measurement of a Nonlocal Entangled Quantum State

Entanglement and the wave function description are two of the core concepts that make quantum mechanics such a unique theory. A method to directly measure the wave function, using weak values, was demonstrated by Lundeen et al. [Nature 474, 188 (2011)]. However, it is not applicable to a scenario of two disjoint systems, where nonlocal entanglement can be a crucial element, since that requires obtaining weak values of nonlocal observables. Here, for the first time, we propose a method to directly measure a nonlocal wave function of a bipartite system, using modular values. The method is experimentally implemented for a photon pair in a hyperentangled state, i.e., entangled both in polarization and momentum degrees of freedom.Received 16 April 2019Revised 24 June 2019DOI:https://doi.org/10.1103/PhysRevLett.123.150402© 2019 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasNonlocalityQuantum entanglementQuantum foundationsQuantum measurementsQuantum opticsWeak values & weak measurementsQuantum InformationGeneral Physics
COMPARATIVE STUDY OF CAPSULE NEURAL NETWORK IN VARIOUS APPLICATIONS

The advancement in the machine learning and the computer vision has caused several improvements and development in numerous of domains. Capsule neural networks are one such machine learning system that imitates the neural system and develops the structures based on the hierarchical relationships. It does the inverse operation of the computer graphic in representing an object by, segregating the object in the image into different part and viewing the in-existing relationship between the each parts to represent in order to preserve even the minute details related to the object, unlike CNN that losses major of the information’s related to the spatial location of the object that are essential in the segmentation and the detection. So the paper presents the comparative study of the capsule neural network in various application, presenting the efficiency of the capsules networks over the convolutional neural networks.
Noise detection and image denoising based on fractional calculus

Fractional-order integral can weaken high-frequency signal and greatly preserve low-frequency signal nonlinearly, that is, the high-frequency noise can be removed while retaining the information of low-frequency image itself, thus the fractional calculus can achieve good a denoising effect and the application of fractional calculus theory in the digital image processing field has also been favored by more and more scholars. On the basis of summarizing and analyzing previous research works, this paper proposes a new method for detecting noise points in images using fractional differential gradient and an improved image denoising algorithm based on fractional integration. The noise detection method determines the noise position by the fractional differential gradient, and achieves to detect the noise, snowflake and stripe anomaly though utilizing the neighborhood information feature of the image and the contour and direction distribution of various noise anomalies in spatial domain; the image denoising algorithm firstly regards the appearance of noise points in image as a small probability event and divides it, then applies the fractional calculus to subtly transform and adjust signal filter and meanwhile utilizes iterative idea to control image denoising effect for accurately distinguishing between noise and high-frequency signals, so that the original image feature information is more preserved while the image is denoised. The simulation results show that this algorithm model can effectively remove the noise while maintaining the details of image edge and texture, and have the characteristics of simple algorithm and good stability. The model can effectively remove the noise and meanwhile preserve the details of image edges and textures, and has the characteristics of simple algorithm and high stability. The study results of this paper provide a reference for further research on the noise detection and image denoising based on fractional calculus.
Colour image encryption scheme based on enhanced quadratic chaotic map

In this study, an enhanced quadratic map (EQM) is proposed and has been applied in a new colour image encryption scheme. The performance evaluations show that the EQM has excellent performances such as better Lyapunov exponent and larger chaotic ranges when compared with the classical quadratic map. The sequences generated from this EQM are successfully used in a new proposed colour image encryption scheme with excellent confusion and diffusion properties. The encryption structure is based on the permutation–diffusion process, and then adopted on the classical permutation, it is characterised by a high speed of diffusion, which enables the encryption of the three components of the plaintext image at the same time, and these encrypted components are simultaneously related to each other. The proposed scheme is tested on the USC-SIPI image dataset and on the real-life image dataset; its effectiveness is also compared with five latterly proposed image encryption schemes. The simulation results indicate that the proposed scheme has the properties of large key space, a weaker correlation between neighbouring pixels, higher sensitivity towards key, greater randomness of pixels and the capacity to withstand statistical analysis, plaintext/chosen-plaintext attacks, and differential attacks, thus that it has higher security and can be appropriate for image encryption.
Potential impact of climate change on solar resource in Africa for photovoltaic energy: analyses from CORDEX-AFRICA climate experiments

Abstract The development of renewable electricity in Africa could be massive in coming decades, as a response to the rapid rising electricity demand while complying with the Paris Agreements. This study shows that in the high-resolution climate experiments of CORDEX-AFRICA, the annual mean solar potential is expected to decrease on average by 4% over most of the continent by the end of the century, reaching up to 6% over the Horn of Africa, as a direct result of decrease in solar radiation and increase in air surface temperature. These projections are associated with large uncertainties, in particular over the Sahel and the elevated terrains of eastern Africa. While the expected decrease may affect the sizing of the numerous solar projects planned in Africa for the next decades, this study suggests that it does not endanger their viability. At last, this study indicates that the design of such projects also needs to account for the non-negligible uncertainties associated with the resource.
Hello quantum world! Google publishes landmark quantum supremacy claim

The company says that its quantum computer is the first to perform a calculation that would be practically impossible for a classical machine. The company says that its quantum computer is the first to perform a calculation that would be practically impossible for a classical machine.
DeepEE: Joint Optimization of Job Scheduling and Cooling Control for Data Center Energy Efficiency Using Deep Reinforcement Learning

The past decade witnessed the tremendous growth of power consumption in data centers due to the rapid development of cloud computing, big data analytics, and machine learning, etc. The prior approaches that optimize the power consumption of the information technology (IT) system and/or the cooling system always fail to capture the system dynamics or suffer from the complexity of system states and action spaces. In this paper, we propose a Deep Reinforcement Learning (DRL) based optimization framework, named DeepEE, to improve the energy efficiency for data centers by considering the IT and cooling systems concurrently. In DeepEE, we first propose a PArameterized action space based Deep Q-Network (PADQN) algorithm to solve the hybrid action space problem and jointly optimize the job scheduling for the IT system and the airflow rate adjustment for the cooling system. Then, a two-time-scale control mechanism is applied in PADQN to coordinate the IT and cooling systems more accurately and efficiently. In addition, to train and evaluate the proposed PADQN in a safe and quick way, we build a simulation platform to model the dynamics of IT workload and cooling systems simultaneously. Through extensive real-trace based simulations, we demonstrate that: 1) our algorithm can save up to 15% and 10% energy consumption in comparison with the baseline siloed and joint optimization approaches respectively; 2) our algorithm achieves more stable performance gain in terms of power consumption by adopting the parameterized action space; and 3) our algorithm leads to a better tradeoff between energy saving and service quality.
Vision-Face Recognition Attendance Monitoring System for Surveillance using Deep Learning Technology and Computer Vision

Nowadays, Artificial Neural networks can be trained over several billion images and can be used to detect and recognize faces with relative ease and flexibility in an instant. This concept is used in the implementation of this real time attendance cum surveillance system that can be prototyped and set into action. Some of the major applications of this innovative method include face attendance using a single snap mode in smartphones for university classes, further real-time facial recognition surveillance of lab facilities and work places which can set this as a first line of defense against intruders from gaining access. The user-friendly graphical user interface provides flexibility and ease in running these powerful face recognition algorithms powered by deep-learning. We have achieved a maximum recognition accuracy of 74 percent while running the real time surveillance algorithm. This work was done as a solution to the absence of a robust and user friendly face recognition attendance system.
Adaptive sliding windows for improved estimation of data center resource utilization

Accurate prediction of data center resource utilization is required for capacity planning, job scheduling, energy saving, workload placement, and load balancing to utilize the resources efficiently. However, accurately predicting those resources is challenging due to dynamic workloads, heterogeneous infrastructures, and multi-tenant co-hosted applications. Existing prediction methods use fixed size observation windows which cannot produce accurate results because of not being adaptively adjusted to capture local trends in the most recent data. Therefore, those methods train on large fixed sliding windows using an irrelevant large number of observations yielding to inaccurate estimations or fall for inaccuracy due to degradation of estimations with short windows on quick changing trends. In this paper we propose a deep learning-based adaptive window size selection method, dynamically limiting the sliding window size to capture the trend for the latest resource utilization, then build an estimation model for each trend period. We evaluate the proposed method against multiple baseline and state-of-the-art methods, using real data-center workload data sets. The experimental evaluation shows that the proposed solution outperforms those state-of-the-art approaches and yields 16 to 54% improved prediction accuracy compared to the baseline methods.