Reviewerly/exHarmony huggingface dataset:
A Novel HDR Image Zero-Watermarking Based on Shift-Invariant Shearlet Transform

In this paper, a novel high dynamic range (HDR) image zero-watermarking algorithm against the tone mapping attack is proposed. In order to extract stable and invariant features for robust zero-watermarking, the shift-invariant shearlet transform (SIST) is used to transform the HDR image. Firstly, the HDR image is converted to CIELAB color space, and the L component is selected to perform SIST for obtaining the low-frequency subband containing the robust structure information of the image. Secondly, the low-frequency subband is divided into nonoverlapping blocks, which are transformed by using discrete cosine transform (DCT) and singular value decomposition (SVD) to obtain the maximum singular values for constructing a binary feature image. To increase the watermarking security, a hybrid chaotic mapping (HCM) is employed to get the scrambled watermark. Finally, an exclusive-or operation is performed between the binary feature image and the scrambled watermark to compute robust zero-watermark. Experimental results show that the proposed algorithm has a good capability of resisting tone mapping and other image processing attacks.
Analysis of a Blockchain-based website using the technology acceptance model: the case of Save Ideas

Nowadays, blockchain applications appear as a new technology which disrupts traditional centralised authorities. Even though this technology has huge potential and receives increasing investments, researches are mostly limited to bitcoin, and they are limited. With subjective knowledge about blockchain and individual trust, this study attempts to focus on the technology acceptance model in order to understand the dynamics behind its use by individuals. A quantitative research study was conducted with 94 users of Save-Ideas.com, which is a blockchain-based website, and the results were analysed by Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings indicate that individuals with knowledge of blockchain technology will have greater trust in these websites and will perceive these websites as more useful. Then, people will be more likely to accept the website.
Frontal and non-frontal face detection using deep neural networks (DNN)

Face recognition has always been one of the most searched and popular applications of object detection, starting from the early seventies. Facial recognition is used for access control, authentication, fraud detection, surveillance, and by individuals to unlock their devices. The less intrusive and robustness of the face detection systems, make it better than the fingerprint scanner and iris scanner. The frontal face can be easily detected, but multi-view face detection remains a difficult task, due to various factors like illumination, various poses, occlusions, and facial expressions. In this paper, we propose a Deep Neural Network (DNN) based approach to improve the accuracy of detection of the face. We show that Deep Neural Networks algorithms have better accuracy than traditional face detection algorithms for multi-view face detection. The Deep Neural Network (DNN) gives more precise and accurate results, as the DNN model is trained with large datasets and, the model learns the best features from the dataset.
Automated Gold Grain Counting. Part 2: What a Gold Grain Size and Shape Can Tell!

Glacial drift exploration methods are well established and widely used by mineral industry exploring for blind deposit in northern territories, and rely on the dispersion of mineral or chemical signal in sediments derived from an eroded mineralized source. Gold grains themselves are the prime indicator minerals to be used for the detection of blind gold deposits. Surprisingly, very little attention has been dedicated to the information that size and shape of gold grain can provide, other than a simple shape classification based on modification affecting the grains that are induced in the course of sediment transport. With the advent of automated scanning electron microscope (SEM)-based gold grain detection, high magnification backscattered electron images of each grain are routinely acquired, which can be used for accurate size measurement and shape analysis. A library with 88,613 gold grain images has been accumulated from various glacial sediment surveys on the Canadian Shield and used to detect trends in grains size and shape. A series of conclusions are drawn: (1) grain size distribution is consistent among various surveys and areas, (2) there is no measurable fine-grained gold loss due to natural elutriation in ablation or reworked till, or during the course of reverse circulation drilling, (3) there is no grain size sorting during glacial transport, severing small grains from large ones, (4) shape modification induced by transport is highly dependent on grain size and original shapes, and (5) the use of grain shape inherited from neighboring minerals in the source rocks is a useful feature when assessing deposit types and developing exploration strategies.
A photo‐based quality assessment model for the estimation of PM2.5 concentrations

Rapid economic growth has caused severe environmental pollution, which has aroused great concern. This pollution affects public health and impairs visibility, therefore, it should be given greater consideration. In this paper, a photo-based PM2.5 concentration predictor is proposed based on the natural scene statistics without artificial assistance or extra information. Given that the quality of PM2.5 concentration images is determined by many factors, three types of influencing factors are analysed: the colourfulness, the structural degradation and the contrast. The first feature consists of the hue, saturation and colour descriptors, which measure the colourfulness of the PM2.5 concentration images. The second feature is determined based on the contrast can effectively portray the quality of PM2.5 concentration in the images. The third feature is extracted based on the natural scene statistics model, which measures the local and global structural degradation information and the naturalness of the PM2.5 concentration images. Finally, the three features are used to train a random forest model that can be used to predict the concentration of PM2.5. Experimental results illustrate that the performance of the proposed model is better than those of popular competitors on AQID.
Testimonies of the Future: The Digital-Turn in the Philosophy of Media and Hybrid Arts

The article provides an overview of several academic disciplines, such as the philosophy of media, theory of art, and visual studies for the analysis of transformation of visual knowledge and image in the digital era. The views of researchers are presented with attention to three largest traditions in the philosophy of media and visual studies — Anglo-Saxon, Russian, and German. During the review, three basic theses are proposed: 1) digital ontologies are close to “flat ontologies” in basic principles of ontology, where the most important is modularity, which cannot be reduced to either hardware or software; 2) the influence of digital era is reflected in the hybrid art, as a result of incorporate high technology and art; 3) the result of digital revolution can be considered as the distinction between analog and digital media. In this case, a new type of digital object (virtual) is understood as the difference between a copy and original work, a new variation of materiality.The prospects for further research on the role of digital objects and hybrid art are as follows: — One of the tasks is a critical analysis of the ontology of a digital object with attention to image productionin the context of postmodern art studies and philosophy; — The second task may be to study the works of new media with attention to the transformation of their institutional relationships, the role of Museum archives, and exhibition practices.
Interpolated binary search: An efficient hybrid search algorithm on ordered datasets

The exponential increase in the rate of data size is much higher than the increase in the speed of the computer, which has given much focus to search algorithms in the research literature. Finding an item in an ordered dataset is an efficient method in the data processing. However, binary and interpolation algorithms are commonly used to search ordered datasets in many applications. In this paper, we propose a hybrid algorithm for searching ordered datasets based on the idea of interpolation and binary search. The proposed algorithm is called Interpolated Binary Search (IBS). It is well known that the performance of traditional interpolation search depends specifically on key distribution, and its performance degrades significantly in non-uniform distributed datasets. Therefore, our proposed algorithm works efficiently on various distribution datasets. In particular, IBS aims to search datasets of unknown distribution or datasets that change dynamically and produce a dynamic distribution. Experimental results show that IBS performs better compared to other algorithms that use a similar approach.
Video Based Sub-Categorized Facial Emotion Detection Using LBP and Edge Computing

Facial expression recognition assumes a significant function in imparting the feelings and expectations of people. Recognizing facial emotions in an uncontrolled climate is more problematic than in a controlled climate due to progress in hindrance, glare and clamor. This paper, we demonstrate another system for successful facial emotion recognition from ongoing face images. Dissimilar to different strategies which invest a lot of energy by partitioning the picture into squares or entire face pictures; our strategy extricates the discriminative component from notable face areas and afterward consolidates with surface and direction highlights for better portrayal. We also made sub-categories in the main expressions like happy and sad, to identify the level of happiness and sadness and to check whether the person is really happy/sad or acting to be happy/sad. Moreover, we lessen the information measurement by choosing the profoundly discriminative highlights. The proposed system is fit for giving high matching precision rate even within the sight of impediments, light, and commotion. To show the heartiness of the expected structure, it utilized two freely accessible testing dataset. These trial results show that the presentation of the expected structure is superior to current strategies, which demonstrate the impressive capability of consolidating mathematical highlights with appearance-based highlights.
Time-Continuous Real-Time Trajectory Generation for Safe Autonomous Flight of a Quadrotor in Unknown Environment

In this paper, we present an efficient global and local replanning method for a quadrotor to complete a flight mission in a cluttered and unmapped environment. A minimum-snap global path planner generates a global trajectory that comprises some waypoints in a cluttered environment. When facing unexpected obstacles, our method modifies the global trajectory using geometrical planning and closed-form formulation for an analytical solution with 9th-order polynomial. The proposed method provides an analytical solution, not a numerical one, and it is computationally efficient without falling into a local minima problem. In a simulation, we show that the proposed method can fly a quadrotor faster than the numerical method in a cluttered environment. Furthermore, we show in experiments that the proposed method can provide safer and faster trajectory generation than the numerical method in a real environment.
Dynamic Path Planning Of Unmanned Surface Vehicle Based On Genetic Algorithm With Sliding Curve Guidance System

Unmanned Surface Vehicle (USV) is an unmanned ship that is controlled through a remote control system (manual) or automatic control system (autopilot), move due to the thrust force from thruster machine and can turning due to the deflection angle of rudder. The USV path planning system becomes an important task so that the ship can make the global trajectory with the minimum travel distance according to the desired navigation while at the same able to avoid various obstacles from local dangerous situations that have the potential for collisions. To be able to do dynamic USV path planning, the Genetic Algorithm method with a sliding curve guidance system and PID MRAC controller is used. The use of this method gives smooth ship track performance with shortest distance in a 400x400 square meter map with static and dynamic obstacles. In a dynamic environment, the path replanning process that takes place in 0,98 seconds is able to find a new path that does not collide the obstacles. For the purposes of algorithm validation, the simulation is performed using MATLAB software with real ship parameters of 6 meters length USV.
Improving Indonesian EFL Learners’ Motivation Through Computer Assisted Learning (CALL)

It has been argued that technology can improve learners’ motivation. Furthermore, technology integration has been regarded one of motivational strategies or techniques to improve learners’ positive attitudes. In this regard, one of motivational strategies that can be given to learners is incorporating CALL (Computer Assisted Language Learning) to EFL (English as a Foreign Language) classrooms. Therefore, the present research analysed the effects of CALL on a group of Indonesian EFL learners’ motivation. In order to obtain the results, the present research conducted a quasi-experimental study toward 40 college students. In addition, a set of questionnaires adapted from Taguchi, Magid, &amp; Papi (2009) as well as semi-structured interviews were used as research instruments. In addition, English tests will be given to the respondents to see the effects of CALL on the improvement of learners’ achievements. The results of present study suggest the positive effects of CALL on learners’ motivation. In addition, learners’ achievement also improved after being intervened with CALL. Nevertheless, the use of CALL needs to be supported by teachers who are capable enough to provide suitable materials that suit learners’ need. Thus, it seems important for teachers to be provided with prior sufficient training on how to use CALL effectively.
Cascaded Filtering Using the Sigma Point Transformation

It is often convenient to separate a state estimation task into smaller "local" tasks, where each local estimator estimates a subset of the overall system state. However, neglecting cross-covariance terms between state estimates can result in overconfident estimates, which can ultimately degrade the accuracy of the estimator. Common cascaded filtering techniques focus on the problem of modelling cross-covariances when the local estimators share a common state vector. This letter introduces a novel cascaded and decentralized filtering approach that approximates the cross-covariances when the local estimators consider distinct state vectors. The proposed estimator is validated in simulations and in experiments on a three-dimensional attitude and position estimation problem. The proposed approach is compared to a naive cascaded filtering approach that neglects cross-covariance terms, a sigma point-based Covariance Intersection filter, and a full-state filter. In both simulations and experiments, the proposed filter outperforms the naive and the Covariance Intersection filters, while performing comparatively to the full-state filter.
Blockchain Technology: Challenges and Opportunities in the World of Indirect Taxes

Blockchain Technology revolutionize the market of digitization. Blockchain technology has also emerged as a solution in case of indirect taxes also. This technology can transform the tax regime and can contribute majorly to digital India. In GST a registered firm has to file 37 returns during a year, this technology will reduce this burden off the vendors as all the transactions are recorded in real time in block chain , which removes the need of filing any return. Transparency of system will remove all the loopholes from the system and tax evasion will also become impossible which will ultimately lead to reduction in frauds and a tampered proof system. Blockchain has brought a lot of excitement and this technology has shown its potential to transform business. But what this technology could do for the world of indirect tax? This paper will look at the challenges and opportunities that lies ahead for government in the world of indirect taxes.
Few-shot Learning for Slot Tagging with Attentive Relational Network

Metric-based learning is a well-known family of methods for few-shot learning, especially in computer vision.Recently, they have been used in many natural language processing applications but not for slot tagging.In this paper, we explore metric-based learning methods in the slot tagging task and propose a novel metric-based learning architecture -Attentive Relational Network.Our proposed method extends relation networks, making them more suitable for natural language processing applications in general, by leveraging pretrained contextual embeddings such as ELMO and BERT and by using attention mechanism.The results on SNIPS data show that our proposed method outperforms other state of the art metric-based learning methods.
SIFT Feature-Based Video Camera Boundary Detection Algorithm

Aiming at the problem of low accuracy of edge detection of the film and television lens, a new SIFT feature-based camera detection algorithm was proposed. Firstly, multiple frames of images are read in time sequence and converted into grayscale images. The frame image is further divided into blocks, and the average gradient of each block is calculated to construct the film dynamic texture. The correlation of the dynamic texture of adjacent frames and the matching degree of SIFT features of two frames were compared, and the predetection results were obtained according to the matching results. Next, compared with the next frame of the dynamic texture and SIFT feature whose step size is lower than the human eye refresh frequency, the final result is obtained. Through experiments on multiple groups of different types of film and television data, high recall rate and accuracy rate can be obtained. The algorithm in this paper can detect the gradual change lens with the complex structure and obtain high detection accuracy and recall rate. A lens boundary detection algorithm based on fuzzy clustering is realized. The algorithm can detect sudden changes/gradual changes of the lens at the same time without setting a threshold. It can effectively reduce the factors that affect lens detection, such as flash, movies, TV, and advertisements, and can reduce the influence of camera movement on the boundaries of movies and TVs. However, due to the complexity of film and television, there are still some missing and false detections in this algorithm, which need further study.
A Unified Framework for Anomaly Detection of Satellite Images Based on Well-Designed Features and an Artificial Neural Network

Nowadays, as the number of remote sensing satellites launched and applied in China has been mounting, relevant institutions’ workload of processing raw satellite images to be distributed to users is also growing. However, due to factors such as extreme atmospheric conditions, diversification of on-board device status, data loss during transmission and algorithm issues of ground systems, defect of image quality is inevitable, including abnormal color, color cast, data missing, obvious stitching line between Charge-Coupled Devices (CCDs), and inconstant radiation values between CCDs. Product application has also been impeded. This study presents a unified framework based on well-designed features an Artificial Neural Network (ANN) to automatically identify defective images. Samples were collected to form the dataset for training and validation, systematic experiments designed to verify the effectiveness of the features, and the optimal network architecture of ANN determined. Moreover, an effective method was proposed to explain the inference of ANN based on local gradient approximation. The recall of our final model reached 81.18% and F1 score 80.13%, verifying the effectiveness of our method.
Stability Analysis of Neural Network Models in Engineering Design

In this paper neural networks applications in engineering design are discussed. The question for stability of their steady states is also considered. Some new efficient criteria are proposed. Since neural networks are relevant systems applied in various engineering design tasks, including many optimization and control problems, the results can be useful in design of such systems of diverse interest.
Semi-U-Net: A Lightweight Deep Neural Network for Subject-Sensitive Hashing of HRRS Images

As a special case of perceptual hashing algorithm, subject-sensitive hashing can realize “subject-biased” integrity authentication of high resolution remote sensing (HRRS) images, which overcomes the deficiencies of existing integrity authentication technologies. However, the existing deep neural network for subject-sensitive hashing have disadvantages such as high model complexity and low computational efficiency. In this paper, we propose an efficient and lightweight deep neural network named Semi-U-net to achieve efficient subject-sensitive hashing. The proposed Semi-U-net realizes the lightweight of the network from three aspects: First, considering the general process of perceptual hashing, it adopts a semi-u-shaped structure, which simplify the model structure and prevent the model from extracting too much redundant information to enhance the robustness of the algorithm; Second, the number of model parameters and the computational cost are significantly reduced by using deep separable convolution in the entire asymmetric network; Third, the number of model parameters is further compressed by using the dropout layer several times. The experimental results show that the size of our Semi-U-Net model is only 5.38M, which is only 1/27 of MUM-net and 1/15 of MultiResUnet. The speed of the Semi-U-Net based subject-sensitive hashing algorithm is 88.6 FPS, which is 2.89 times faster than MultiResUnet based algorithm and 2.1 times faster than MUM-net Based Algorithm. FLOPs of Semi-U-net is only 1/28 of MUM-net and 1/16 of MultiResUnet.
Blockchain Disrupting Fintech and the Banking System

Nowadays society is profoundly changed by technology, velocity and productivity. While individuals are not yet prepared for holographic connection with banks or financial institutions, other innovative technologies have been adopted. Lately, a new world has been launched, personalized and adapted to reality. It has emerged and started to govern almost all daily activities due to the five key elements that are foundations of the technology: machine to machine (M2M), internet of things (IoT), big data, machine learning and artificial intelligence (AI). Competitive innovations are now on the market, helping with the connection between investors and borrowers—notably crowdfunding and peer-to-peer lending. Blockchain technology is now enjoying great popularity. Thus, a great part of the focus of this research paper is on Elrond. The outcomes highlight the relevance of technology in digital finance.