Reviewerly/exHarmony huggingface dataset:
Analysis and reduction of the phase error caused by the non-impulse system psf in fringe projection profilometry

The fringe projection profilometry (FPP) has obtained increasing popularity in the field of industrial automation, inverse engineering and graphics. Recent literature reveals that the non-ideal system point spread function (PSF) in FPP will cause phase error in the area around the discontinuous edge. Existing error reduction methods need to detect the location of all the edges accurately first, which are hard to accomplish when the camera is defocused. Meanwhile, the corrected data in the error area relies heavily on the data in its nearest unaffected area, which makes the corrected data unreliable. We prove that the non-ideal system PSF will also induce phase error in the area of surface details, and this is a problem seldom discussed in FPP. In this work, the relationships between the PSF-induced phase error and system parameters are deduced mathematically and numerically. Additionally, a deconvolution-based method is proposed to reduce the PSF-induced phase error in this paper. The proposed method can overcome the shortcomings of the existing approaches. Both simulation and experiment results show that our proposed method can reduce the Root Mean Square phase/height error by up to 4 times, and can also improve the measured 3D details significantly.
Evaluation of hydrocarbon potential using fuzzy AHP-based grey relational analysis: a case study in the Laoshan Uplift, South Yellow Sea, China

Abstract Effective evaluations of hydrocarbon potential contribute to delineating promising target areas for further exploration. Sparse available data and known hydrocarbon reservoirs in frontier areas present considerable challenges to the weighting of geological factors when evaluating hydrocarbon potential. This study proposes a hydrocarbon potential mapping method that employs grey relational analysis (GRA) based on a fuzzy analytic hierarchy process (fuzzy AHP). GRA is a comprehensive evaluation method that represents the hydrocarbon favourability according to the proximity between evaluation targets and the ideal target based on multiple evaluation factors and weights among them. To overcome the uncertainty and vagueness in the weighting procedure, the fuzzy AHP technique relies on experts’ knowledge to define the relative importance of evaluation factors and exploits triangular fuzzy numbers to simulate experts’ judgements in pairwise comparisons. The fuzzy AHP-based GRA method was tested using an example in the Laoshan Uplift in the South Yellow Basin of China. This test application not only quantified the favourability of local traps but also revealed their spatial variations on favourability maps and indicated potential targets for further exploration. The results obtained by the fuzzy AHP-based GRA method were more reliable than that of entropy weight-based GRA and displayed a suitable consistency with known geological information, thus demonstrating that such a procedure could reveal the potential spatial features of hydrocarbon accumulations and support the evaluation of hydrocarbon potential in relatively unknown areas.
How Futures Studies and Foresight Could Address Ethical Dilemmas of Machine Learning and Artificial Intelligence

Drawing from ethical concerns raised by communities of machine learning developers and considering predictive analytics’ very short-term predictions, several futures studies techniques are examined to offer some insights about possible bridges between machine learning and foresight. This review develops three main sections: (1) a brief explanation of central concepts, such as big data, machine learning, and artificial intelligence, hopefully not too simplistic but readable for larger audiences; (2) a discussion about ethical issues, such as bias, discrimination, and dilemmas in research; and (3) a brief description of how futures studies could address ethical dilemmas derived from different time horizons among machine learning immediate results, forecasting short-term predictions, and foresight long-term scenarios.
Discovering influential factors in variational autoencoders

In the field of machine learning, it is still a critical issue to identify and supervise the learned representation without manually intervening or intuition assistance to extract useful knowledge or serve for the downstream tasks. In this work, we focus on supervising the influential factors extracted by the variational autoencoder (VAE). The VAE is proposed to learn independent low dimension representation while facing the problem that sometimes pre-set factors are ignored. We argue that the mutual information of the input and each learned factor of the representation plays a necessary indicator of discovering the influential factors. We find the VAE objective inclines to induce mutual information sparsity in factor dimension over the data intrinsic dimension and therefore result in some non-influential factors whose function on data reconstruction could be ignored. We show mutual information also influences the lower bound of VAE's reconstruction error and downstream classification task. To make such indicator applicable, we design an algorithm for calculating the mutual information for VAE and prove its consistency. Experimental results on MNIST, CelebA and DEAP datasets show that mutual information can help determine influential factors, of which some are interpretable and can be used to further generation and classification tasks, and help discover the variant that connects with emotion on DEAP dataset.
BHCDR: Real-Time Bangla Handwritten Characters and Digits Recognition using Adopted Convolutional Neural Network

Machine learning algorithm suffers to recognize the Bangla handwriting from images because of the complex design, diversities among different writers and similarity between characters and digits. In recent times, deep learning is becoming very popular among the researchers for Bangla Handwriting Recgnition (BHR) because of its high efficiency i n t erms of memory, time complexity and robust feature extraction. This research aims at improving the performance of baseline Convolutional Neural Network (CNN) by increasing the recognition accuracy with minimizing the computational overhead; this paper presents a real-time Bangla Handwritten Characters and Digits Recognition (BHCDR) system using adopted CNN. Our proposed preprocessing technique, data augmentation and incorporating dropout filters i n t he b aseline C NN a rchitecture h ave achieved the goal. The proposed eight layered architecture has used two convolutional layers followed by two Maxpooling layers with 25% dropout filters from one layer to another and two fully connected layers with 50% dropout followed by a softmax classifier. The proposed model is trained and tested using 118,698 images of Bangla lekha-isolated dataset and 21000 images of CMATERdb dataset for Bangla hand-written characters and digits maintaining the ratio of 4:1 respectively. The proposed model has achieved the mean accuracy of 97.43% for classification with the average computational costs of 44.95 ms/f.
Mobile Robot Assistance for Disabled and Senior Citizens Using Hand Gestures

Paralysis (paraplegia) and motor-impairments impact the autonomy of elder people while performing their tasks independently. To fill the gap between machines and humans, gesture plays a significant role. This work focuses on Human-Robot Interaction (HRI), designed for assistance of wheel chair bound people with the help of mobile robots. People will get their work done faster and effortlessly with the help of mobile robots. This paper explains the interaction of mobile robot based on user gestures and assists using a 2-DoF manipulator for pick and place of the objects. Gestures are recognized by camera at real-time and robot (Firebird V-LPC2148) is controlled wirelessly through XBee radio module interface in the given environment.
Prediction of Software Defects Using Automated Machine Learning

The effectiveness of defect prediction depends on modeling techniques as well as their parameter optimization, data preprocessing and ensemble development. This paper focuses on auto-sklearn, which is a recently-developed software library for automated machine learning, that can automatically select appropriate prediction models, hyperparameters and data preprocessing techniques for a given data set and develop their ensemble with optimized weights. In this paper we empirically evaluate the effectiveness of auto-sklearn in predicting the number of defects in software modules. In the experiment, we used software metrics of 20 OSS projects for cross-release defect prediction and compared auto-sklearn with random forest, decision tree and linear discriminant analysis by using Norm(Popt) as a performance measure. As a result, auto-sklearn showed similar prediction performance as random forest, which is one of the best prediction models for defect prediction in past studies. This indicates that auto-sklearn can obtain good prediction performance for defect prediction without any knowledge of machine learning techniques and models.
Applied to Mobile Multimedia Intelligent Speech System Interactive Topic Guiding Model

Recently, the rapid development of mobile multimedia intelligent voice systems has affected the rapid changes in user interaction. Meanwhile, the continuous optimization of recommended algorithms makes the change of interaction mode from the original user's active search to the machine actively recommending continuously. It is an inevitable trend that multimedia intelligent voice dialogue system brings more efficient content services to users through active recommendation. However, the existing multimedia intelligent voice dialogue systems are mainly based on the user's expression content to analyze the user's subject willingness and then provide the reply contents to user. This kind of interaction only drives the topic through the user, which will cause some problems like single chat topic and missing points of interest. In order to address these problems, this paper proposes an interactive topic guiding model. This model uses the surrounding similar users and user context information to guide users' topics, and taking into account the harmony of human-computer interaction, the game method is adopted to select the optimal content response strategy by considering the user's and robot's emotional friendliness and content friendliness. The experiments show that the model proposed in this paper can effectively guide topics. Moreover, it has significant improvement in content richness compared with other models in human-machine natural interaction, which can provide some research ideas for guiding the topic in the mobile multimedia intelligent voice system.
IoT and Blockchain for Smart Locks

Community-based online platforms for hospitality services have connected hosts and guests globally. With the increasing popularity of those platforms - e.g., Airbnb - some management issues have attracted the attention of researchers - for instance, granting access to properties and rooms remotely, without requiring hosts and guests to meet in person. Solutions have been proposed - e.g., locks with pin pad and vendor centralized smart locks - but they usually have shortcomings that compromise either security, privacy, or convenience. This research designs and proposes a blockchain-based system for smart door locks to provide the convenience of remote access control management, while security and privacy for both hosts and guests are not compromised. Moreover, to surpass current locks' functionalities, this research proposes a feature that enables the guests to cease the hosts' access to the lock, during their stay. This feature also guarantees to the guest that no one will be able to change that access rule without their explicit approval. The proposed solution integrates Ethereum blockchain as the foundation of the system and uses Infura API as the bridge to connect the IoT infrastructure to the blockchain network. Such architecture alleviates hardware demand from the equipment, which can favor the use of resource-constrained IoT devices. Once Ethereum is used to build the solution, and users are charged fees to perform some actions in the blockchain, the system is evaluated concerning operation costs. Three Ethereum test networks - Ganache, Ropsten, and Rinkeby - were used to run the smart contract and assess the charge to complete significant actions in the system. The results show that the cost to use the smart lock is low, especially if the benefits of security, privacy, and convenience are taken into consideration. Most of the actions yielded fees about cents of a US dollar, where the exception is a one-time payment of USD3.83 - worst-case scenario - to deploy the smart contract - i.e., setup the system.
Breast cancer classification using a constructed convolutional neural network on the basis of the histopathological images by an interactive web-based interface

In this study, it is aimed to develop a system that can provide clinical support to physicians in the diagnosis of breast cancer with open source access artificial intelligence based software. The proposed system was designed using an open source data set for the classification of breast cancer (benign/malignant) on the basis of the histopathological images. In this context, Keras library and convolutional neural networks from deep learning methods were used on the images obtained by staining with hematoxylin and eosin of biopsy specimens taken from breast tissues. Shiny package in the R programming language is employed to develop for the user interface. According to the experimental results obtained from the study, it was determined that the designed system gives promising predictions in the classification of breast cancer and can be used for clinical decision support in the classification of the disease. This designed system can be available at http://biostatapps.inonu.edu.tr/MKSY/ in both English and Turkish.
Background modeling and foreground extraction in video data using spatio-temporal region persistence features

The proposed work describes an efficient methodology for adaptive background modeling and foreground extraction from video data using the newly proposed Spatio-temporal region persistence (STRP) descriptor. The STRP background descriptor includes block-wise statistics of intensity bins and their temporal persistency. Blockwise feature extraction helps to consider the local changes, while intensity bins provide consistent output for a group of similar intensities in an intra-regional sense. In this work, we have tried to minimize the effect of different video irregularities like dynamic background, ghosting effect, change in illuminations, video noise, etc. Additionally, adaptive threshold selection and regular adjustment of modeled background descriptors make the procedure robust. Two benchmark datasets, Changed Detection and Scene Background Modeling, and Initialization (SBMI) have been used to verify the efficiency of our work. The results and comparative studies with the related works justify the effectiveness of our proposed technique.
Deep Learning (Partly) Demystified

Successes of deep learning are partly due to appropriate selection of activation function, pooling functions, etc. Most of these choices have been made based on empirical comparison and heuristic ideas. In this paper, we show that many of these choices - and the surprising success of deep learning in the first place - can be explained by reasonably simple and natural mathematics.
SAR image super-resolution using deep residual SqueezeNet

Enhancing the resolution of SAR image can significantly improve more complicated application such as automatic target detection. This paper proposes a new algorithm to reconstruct super-resolution image by using a deep residual SqueezeNet. It directly achieves end to end mapping from the low-resolution image to a high-resolution image with the convolutional neural network. The lightweight SqueezeNet with fewer parameters but equivalent accuracy compared to a well-known model is used to reduce the computational complexity. Moreover, inspired by ResNet, we utilize the residual learning method with high learning rates to accelerate convergence. Experimental results demonstrate that the new algorithm brought forward in this paper not only has lightweight construction, but also can be faster, and get similar or better results than state-of-the-art methods.
A Brief Analysis of Amazon Online Reviews

Shifting from traditional marketing into online marketing has allowed people to share their experiences about various aspects of those products using textual comments known as Product Reviews. As a result of this shifting, people are able to access various websites where they can find reviews for all kind of products, even the rare ones. Thus, these reviews act as a supplementary information and help people to make the right decision before buying products. Reviews that influence one's decision are considered influential reviews, as they provide truthful experiences. Given the list of reviews for a certain product, each user can vote for any given review as helpful or unhelpful. As a result, each review would be given a number that represents how many users found this review helpful. This would indicate how influential each review is. As a result, buyers rely on these reviews and those who wrote these reviews. This study emphasizes on the importance of using user votes as an important source of information for new users. The contribution of this work lies in two aspects. First, it provides a comprehensive statistical analysis of a previously-published dataset containing Amazon reviews. Second, this study insists on the importance of using user votes. This study is the first phase for many future interesting directions. It was shown that the relationship between the number of reviews and the percentage of votes is an inverse relationship.
OCEAN: A multi agent system dedicated to knowledge management

Emphasis on knowledge and information is one of the challenges of the 21st century to differentiate the intelligent business enterprises. Enterprises have to develop their organization in order to capture, manage, and use information in a context of continually changing technology. Indeed knowledge and information are completely distributed in the information network of the company. In addition, knowledge is by nature, heterogeneous since it is provided from different information sources like the software, the technical report, the meeting statements, etc. We present in this paper the architecture of a multi-agent system, which allows the capitalization of the distributed and heterogeneous knowledge. We then present how the agents help business experts to design ontologies in detailing this problematic and how the agents extract knowledge from different users’ databases by using a semantic approach.
Reasoning about a Machine with Local Capabilities

Capability machines provide security guarantees at machine level which makes them an interesting target for secure compilation schemes that provably enforce properties such as control-flow correctness and encapsulation of local state. We provide a formalization of a representative capability machine with local capabilities and study a novel calling convention. We provide a logical relation that semantically captures the guarantees provided by the hardware (a form of capability safety) and use it to prove control-flow correctness and encapsulation of local state. The logical relation is not specific to our calling convention and can be used to reason about arbitrary programs.
Relation constrained attributed network embedding

Network embedding aims at learning a low-dimensional dense vector for each node in the network. In recent years, it has attracted great research attention due to its wide applications. Most existing studies model the graph structure only and neglect the attribute information. Although several attributed network embedding methods take the node attribute into account, they mainly focus on the basic relations between the nodes and their attributes like a user and his/her interests (attributes). The composite relations between two nodes, and two nodes’ attributes, and the related nodes and their attributes, contain rich information and can enhance the performance of many network analysis tasks. For example, two scholars having the common interests as “nature language processing” and “knowledge graph” may collaborate in the future and there will be a new edge in the network. However, such important information is still under-exploited. To address this limitation, we propose a novel framework to exploit the abundant relation information to enhance attributed network embedding. The main idea is to employ the multiple types of relations in attributed networks as the constraints to improve the network representation. To this end, we first construct the composite relations between two nodes and their attributes in addition to the commonly used basic relations. We then develop a relation constrained attributed network (RCAN) framework to learn the node representations by constraining them with these relations. We conduct extensive experiments on three real-world datasets to show the effectiveness of our proposed RCAN as an attributed network embedding method for modeling various social networks. The results demonstrate that our method achieves significantly better performance than the state-of-the-art baselines in both the link prediction and node clustering tasks.
Pose-Informed Face Alignment for Extreme Head Pose Variations in Animals

Landmark localisation is a vital step in automatic analysis of facial expressions of animals. Head motion is one of the most challenging problems for face alignment for humans and animals. For animals this is exacerbated by the increased amounts of self-occlusion resulting from variations in head pose. In this paper, we present a novel model for detection of an extensive set of facial landmarks for sheep. A dataset of 850 sheep facial images, annotated with a 25 facial landmark scheme and occlusion information, is introduced: the Sheep Facial Landmarks in the Wild (SFLW) dataset, including a wide range of variations in head-pose and occlusion. Data augmentation techniques are introduced using thin-plate-spline warping and negatively correlated augmentation to boost representation of extreme head poses. We then present a novel pose-informed landmark localisation method based on a fine-tuned CNN model for human head pose estimation. This method is shown to significantly outperform the existing state-of-the-art approach on the introduced SFLW dataset and the viability of the technique for real-world use is demonstrated through the implementation of a near real-time video pipeline.