Reviewerly/exHarmony huggingface dataset:
Notice of Violation of IEEE Publication Principles: Tone Mapping Beyond the Classical Receptive Field

Notice of Violation of IEEE Publication Principles<br><br> "Tone Mapping Beyond the Classical Receptive Field,"<br> by Shao-Bing Gao, Min-Jie Tan, Zhen He, and Yong-Jie Li<br> in the IEEE Transactions on Image Processing, vol. 29, February 2020, pp. 4174‐4187<br><br> After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles. This paper contains significant portions of content from the paper cited below. The original content was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.<br><br> Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following paper<br><br> "Colour Constancy Beyond the Classical Receptive Field,"<br> by Arash Akbarinia, and C. Alejandro Parraga,<br> in the IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 9,September 2018, pp. 2081‐2094<br> <br/> Some neurons in the primary visual cortex (V1) of human visual system (HVS) conduct dynamic center-surround computation, which is thought to contribute to compress the high dynamic range (HDR) scene and preserve the details. We simulate this dynamic receptive field (RF) property of V1 neurons to solve the so-called tone mapping (TM) task in this paper. The novelties of our method are as follows. (1) Cortical processing mechanisms of HVS are modeled to build a local TM operation based on two Gaussian functions whose kernels and weights adapt according to the center-surround contrast, thus reducing halo artifacts and effectively enhancing the local details of bright and dark parts of image. (2) Our method uses an adaptive filter that follows the contrast levels of the image, which is computationally very efficient. (3) The local fusion between the center and surround responses returned by a cortical processing flow and the global signals returned by a sub-cortical processing flow according to the local contrast forms a dynamic mechanism that selectively enhances the details. Extensive experiments show that the proposed method can efficiently render the HDR scenes with good contrast, clear details, and high structural fidelity. In addition, the proposed method can also obtain promising performance when applied to enhance the low-light images. Furthermore, by modeling these biological solutions, our technique is simple and robust considering that our results were obtained using the same parameters for all the datasets (e.g., HDR images or low-light images), that is, mimicking how HVS operates.
Face-Age Modeling: A Pattern Recognition Analysis for Age Estimation

This One way to explain the concept of face recognition is to see it as the computational ability engaged in verifying the identity of a person on the basis of the face image regardless of background, face-attachment, face-makeup and illumination. Face recognition has been researched and applied in real life problems such as security access controls, banking identity verification, immigrant verification and licensing. This study examines popular techniques and approaches involved in face recognition and its application in age estimation. The identification characteristics possessed by the face with other demographic information such as age, sex and ethnicity can be predicted using facial features extracted from facial images. The application of face recognition in these areas especially age estimation has not been richly researched. Consequently, this study presents an explorative write-up of face-age models with reference to the application of the commonly used face recognition technique to computationally achieve age prediction. In this study, it's germane to consider face recognition techniques since an important part of age estimation pipeline is the feature extraction which is also important in face recognition. This study shows that most of the researches in age estimation were tested with image taken under controlled or semi-controlled environment which are not sufficient to capture challenges in real life conditions. It was observed that most existing researches uses FG-NET and MORPH 2 face database in testing age estimation systems with scarcity of black face feature across several existing systems. However, more work is desirable, owing to failure of some system to accurately recognize or identify specific faces or using long turnaround processing time for identification vis-a-vise age estimation. This calls for the development of robust black-face database for local testing and implementation among African population. It is also evident that more work is required to show the interaction of epigenetics and its analysis in face-age modeling.
MBFN

We present a multi-task algorithm for simultaneous face identification, gender recognition, age estimation, several facial attributes prediction and facial expression classification by using a single deep multi-branch convolutional neural network (CNN) based on MobileFaceNet. The proposed method, called MBFN (multi-branch face network), uses the backbone network for face identification and pulls branches from the backbone for other facial tasks. It exploits the synergy among the facial tasks which enhances their individual performances. Extensive experiments show that the proposed network is able to capture global and local information in faces and achieve state-of-the-art result for most of these tasks.
The addition and subtraction of quantum matrix based on GNEQR

The efficient quantum circuits of arithmetic operations are important to perform quantum algorithms. To implement efficient matrix operations, we first modify the generalized model of the novel enhanced quantum representation of digital images (GNEQR) to store unsigned and signed integer matrices. Next, we design the circuits of the circuits of quantum addition, quantum modulo addition, quantum subtraction, and quantum modulo subtraction, these operations all keeping two operands unchanged. Then, we propose the circuits of quantum matrix addition, quantum matrix modulo addition, quantum matrix subtraction, and quantum matrix modulo subtraction for the first time. Furthermore, we present a simulation method to verify the correctness of the proposed arithmetic operations of matrix. The results of simulation experiment show that the propose arithmetic operations of matrix are efficient and correct.
Design and Implementation of Chinese Chess Based on Manipulator

With the development of artificial intelligence, artificial intelligence + robots have slowly entered people's field of vision. In this paper, we apply robots to Chinese chess. The design is based on the microcomputer as the control center, and through the serial communication transmission to the robot arm, by the robot arm to complete the robot chess; In terms of visual recognition, we use hough transform and character recognition to classify and identify chess pieces; In terms of chess strategies, this paper adopts alpha-beta search engine algorithm to conduct simulation experiments. The 6-layer search algorithm can complete the calculation within 5S~10S, the robotic arm movement of each step can be completed within 10S~12S, and the identification of chess pieces by taking photos can be completed within 2S.
The Hybrid Deep Learning GMDH-neo-fuzzy Neural Network and Its Applications

The hybrid deep learning GMDH- neo-fuzzy network was suggested and investigated. The application of GMDH based on self-organization enables to build optimal structure of neo-fuzzy system and train weights of neural network in one procedure. As a node of hybrid neo-fuzzy system neo-fuzzy network with small number of adjustable parameters is suggested. This enables to cut training time and accelerate convergence of training. The experimental studies of GMDH-neo-fuzzy network were carried out in the task of forecasting in macro-economy and financial sphere. The forecasting efficiency of the suggested hybrid neo-fuzzy network was estimated and its sensitivity to variation of tuning parameters was investigated. The suggested approach allows to prevent the drawbacks of deep learning such as vanishing or exploding gradient.
Functionally-Predefined Kernel: a Way to Reduce CNN Computation

Convolutional Neural Networks (CNNs) have achieved high classification accuracy in image recognition, and now, they are widely used for numerous applications. For higher accuracy or more advanced applications, CNNs need to consume tremendous computational resources and time. Hence, many studies for reducing the computational cost of CNNs are actively being conducted. However, many previous methods for reducing the computational cost lead to a non-negligible loss in output accuracy. Therefore, it is still a challenge to reduce the computational cost of CNNs with keeping the output accuracy high. In this paper, we propose a novel concept "Functionally-Predefined Kernel" to reduce the computational cost for CNN training and discuss the potential of computation reuse to reduce the computational cost for CNN inference. Our experimental results show that the number of parameters to be trained can be significantly reduced by utilizing Functionally-Predefined Kernels without accuracy loss. In addition, we revealed that CNN's inference process includes many convolution operations with the same inputs and computation reuse, therefore, has high affinity to CNN computation.
Geochemistry of leptosols and fluvisols in the fast growing city of Benguela (Angola) and assessment of potential risks

In Benguela, urbanization is rapid and uncontrolled, but at the same time industrialization is poorly developed. In the more recently populated areas, the waste collection systems are very poor or absent, there is also an increase in traffic, as well as the use of fossil fuels due to the problems of electrical energy supply, whereas, at the same time industrialization is poorly developed. The Calcaric Leptosol (Protocalcic) is developed on marly‑carbonated formations, while Orthofluvic Fluvisol (Arenic) and Orthofluvic Fluvisol (Arenic Prothotecnic) are developed on detrital formations of quaternary age. The city occupies the Fluvisols, but is expanding to the Leptosol. The Orthofluvic Fluvisol (Arenic Prothotecnic) is heavily alkaline (the average pH is 8.7), the Calcaric Leptosol (Protocalcic) and the Orthofluvic Fluvisol (Arenic) are moderately alkaline (pH 8.1 and 7.8, respectively). Electrical conductivities are 2135, 580 and 1358 μS/cm in Orthofluvic Fluvisol (Arenic Prothotecnic), Orthofluvic Fluvisol (Arenic) and Calcaric Leptosol (Protocalcic), respectively. The potentially toxic elements in Orthofluvic Fluvisol (Arenic Prothotecnic) are: Cu: 76.1 mg/kg, Zn: 398 mg/kg, Sn: 10.7 mg/kg, Sb: 0.93 mg/kg, Cd: 5.36 mg/kg and Pb: 249 mg/kg. The rare earth elements (REE) contents were related to Fe-oxy-hydroxides (r = 0.84). Principal Components Analysis (PCA) demonstrate the association of light REE with secondary phosphates in the Fluvisols. The PCA indicated that P, Cu, Pb, Sb, Sn, Zn contents in Orthofluvic Fluvisol (Arenic Prothotecnic) are due to urban impact. The average of the enrichment factors in the samples from this soil indicates that, the recent urbanization strongly lowers the soil quality of this poorly industrialized, expanding city and that the soil in the urban area is contaminated by Cd, Cu, Pb and Zn, according to the South African, Dutch and Canadian legislation. Preliminary assessment of risks to human health of Benguela residents show that there is still not, non-cancer risk for these metals and there is not carcinogenic risk for Cd, but Pb can be of concern.
Identification of a Speaker from Familiar and Unfamiliar Voices

All human voices carry information about a person that allow us to identify individuals. Most of us can remember voices that surround us that are 'unique' in some way. In this paper, we examine the identification of familiar and unfamiliar voices. The observations are made by analysing the data, understanding the differences between familiar and unfamiliar voices and investigating how the identification is processed in the human brain. The paper describes the ability of humans to identify a speaker through learning and processing information.
Kalman estimator based on BP algorithm for the system with colored and correlated noises

For the linear stochastic system with correlated and colored noises, the augmented state approach and measurement differencing method are presented to obtain the standard state space model with one-correlated white noises. For this system, the Kalman estimator is presented by applying the recursive projection formula and Kalman filtering theory, which include the Kalman filter, one-step predictor and one-step smoother. The BP network algorithm is applied to improve the estimation accuracy, where the estimation covariance, gain and innovation are taken as the input. An example of tracking system verifies the effectiveness of the presented Kalman estimator.
MultiLock

While traditionally smartphones have relied on methods such as a passcode or pattern-based authentication, biometric authentication techniques are gaining popularity. However current biometric methods are heavily dependent on various environmental factors. For example, face authentication methods depend on lighting conditions, camera shake and picture framing, while fingerprint scanning relies on finger placement. All of these variables can result in these systems becoming time-consuming for the user to use. To remedy these problems, we propose MultiLock, a passive, graded authentication system, which uses face authentication as a case study to propose a system that gives users access to their devices without requiring them to manually interact with the lock screen. Multi-Lock allows a user to categorize applications into various security bins based on their sensitivity. By doing so MultiLock can grant users access to different sensitivity applications, based on varying degrees of sureness that the device is being used by its rightful owner. Thus, allowing the device to be used even in adverse lighting conditions without hampering user experience. In our tests, MultiLock was able to grant access to users for 88% of the interactions on average, while passively running in the background. While we use face authentication as an example to demonstrate and propose MultiLock, our system can be used with any confidence based biometric system.
Investigating the Streaming Algorithms Usage in Website Fingerprinting Attack Against Tor Privacy Enhancing Technology

Website fingerprinting attack is a kind of traffic analysis attack that aims to identify the URL of visited websites using the Tor browser. Previous website fingerprinting attacks were based on batch learning methods which assumed that the traffic traces of each website are independent and generated from the stationary probability distribution. But, in realistic scenarios, the websites' concepts can change over time (dynamic websites) that is known as concept drift. To deal with data whose distribution change over time, the classifier model must update its model permanently and be adaptive to concept drift. Streaming algorithms are dynamic models that have these features and lead us to make a comparison of various representative data stream classification algorithms for website fingerprinting. Given to our experiments and results, by considering streaming algorithms along with statistical flow-based network traffic features, the accuracy grows significantly.
Mixed Reality for Stress Relief

Studies have shown that a majority of workers experience stress in the workplace. We envision a product that could help workers relieve stress by making use of Mixed Reality to help them change their immediate vicinity to relieve stress. The design is grounded in our insight from research that people use varying activities to fight stress which helps them in switching context and doing something cognitively less demanding. This is achieved through a wearable headset that would help wearers change the reality in their vicinity at workplace whenever stress levels are high.
Weighted area coverage of maritime joint search and rescue based on multi-agent reinforcement learning

Maritime search and rescue is the last line of defense to ensure the safety of life and property at sea. To solve the problem of cooperative planning of regional coverage task for multiple search and rescue equipment in the actual search and rescue process. Firstly, we propose a weighted region model mapped by the Monte Carlo drift prediction model. And then we use three reinforcement learning algorithms (Q-learning, Sarsa, Sarsa (lambda)) for comparative experiments. Finally, three algorithms are evaluated per the coverage and repetition rate, and the experimental results show that Sarsa coverage rate is higher and repetition rate is lower. We have solved the weighted area coverage problem in the real search and rescue process, greatly improving the decision-making efficiency of the system, and making the search with the highest probability, high search coverage and low repetition rate, which has extremely high practical value.
Graph Convolutional Networks with Objects for Skeleton-Based Action Recognition

Recently, graph convolutional neural networks has become a research hotspot for skeleton-based action recognition because of its excellent performance on graph structure data. Compared to traditional methods, it can explicitly exploit the natural connectivity among the joints and improve greater expressive power. In this paper, we propose a two-stream graph convolutional networks with objects for skeleton-based action recognition. An algorithm is designed for matching similar skeleton in adjacent frames, so that we can get the right skeletons which belong to the same person. It performs well when there are other irrelevant persons in the scene. In addition, other features are less employed except for the human joint in skeleton-based action recognition. We introduce limbs orientation information and related objects information. The related objects are treated as joint points which link with hands. The two-stream networks are built to model coordinate features and orientation features respectively, the results of two streams are fused to one. We get good results on the Kinetics dataset with our methods.
Towards Content Independent No-reference Image Quality Assessment Using Deep Learning

The study of image quality assessment (IQA) is divided on natural scene and document images which are processed using different models and quality metrics. This casts challenges for the development of content-independent no-reference (NR) IQA models which can operate on different types of images without requiring information regarding the content of the images. In this paper we propose a unified no-reference image quality assessment (UIQA) model using a deep learning approach, where a generalization of NR IQA across natural scene and document images is achieved using a deep convolutional neural network (DCNN). Without having to discriminate the type of the images, the proposed model can assess the quality of natural scene and document images in a blind and uniform manner. Testing results on two benchmarking datasets demonstrate that the proposed model achieves promising performances competitive with the state-of-the-art simultaneously on natural scene and document images.
Text Classification Using Long Short-Term Memory

Text classification usually has the basic problem of presenting data with very high dimension, so that the model formed is usually hampered because (1) training time increases exponentially appropriate with the number of feature used and (2) Model has an increased risk of overfitting with a growing number of features. Recurrent Neural Network (RNN) is one of the most popular architectures used in natural language processing (NLP) since the recurrent structure is very suitable for long variable text processing. One of the deep learning methods proposed in this study is RNN with the application of the Long Short-Term Memory (LSTM) architecture. Long Short-Term Memory (LSTM) offers element which is expected to be able to record a feature of input, such as in natural language processing for English, an element record gender of the subject, other element records whether single or plural subject. These features will be found by LSTM itself in the training process. With variant word sequence features, the results of this study have the highest accuracy of 82.13%.
Insurance and inequality in Sub‐Saharan Africa: policy thresholds

Abstract In this study, we examine how insurance affects income inequality in Sub‐Saharan Africa, using data from 42 countries during the period 2004–2014. Three inequality variables are used, namely: the Gini coefficient, the Atkinson index and the Palma ratio. Two insurance premiums are employed, namely: life insurance and non‐life insurance. The empirical evidence is based on the Generalised Method of Moments (GMM). Life insurance increases the Gini coefficient and increasing life insurance has a net positive effect on the Gini coefficient and the Atkinson index. Non‐life insurance reduces the Gini coefficient and increasing non‐life insurance has a net positive effect on the Palma ratio. The analysis is extended to establish policy thresholds at which increasing insurance premiums completely dampen the net positive effects. From the extended analysis, 7.500 of life insurance premiums (percentage of GDP) is the critical mass required for life insurance to negatively affect inequality, while 0.855 of non‐life insurance premiums (percentage of GDP) is the threshold required for non‐life insurance to negatively affect inequality. Policy thresholds are provided at which insurance penetration decreases income inequality in Sub‐Saharan Africa.
Remote Collaborative Knowledge Discovery for Better Understanding of Self-tracking Data

Wearable self-tracking devices are an increasingly popular way for people to collect information relevant to their own health and well-being, but maximising the benefits derived from such information is hindered by the complexity of analysing it. To gain deeper insights into their own information generated by such products, a user with no data analysis expertise could collaborate with someone who does have the required knowledge and skills. To achieve such a successful collaboration, several tasks need to be completed: finding a collaborator, negotiating the terms of the collaboration, obtaining the necessary resources, analysing the data and evaluating the results of the analysis. To support the execution of these tasks, we have developed and deployed an online software platform that enables data collectors and owners to find experts and collaborate with them so they can extract additional knowledge from the self-tracking data. The functionality and user interface of the platform are demonstrated by presenting an application scenario where a data owner shares their sleep data with an expert who applies periodicity analysis to discover cyclical patterns from the data.