Reviewerly/exHarmony huggingface dataset:
Analysis of Watermarking Techniques with Wavelet and Cosine Transform for Imperceptibility on Image

Some medical image watermarking techniques utilize frequency transform domains to improve the performance of the watermarking technique, where the main requirement of the medical image is to maintain the visual quality. This paper analyzes the imperceptibility performance of two frequency transform techniques Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT). The analysis methodology is an experiment the embedding watermark data in the transferred domain of the medical image by image by utilizing two techniques DWT and DCT. The performance of both techniques was measured in terms of image imperceptibility and embedding capacity. The imperceptibility performance based on Peak Signal-toNoise Ratio (PSNR) and Bit Error Rate (BER) values of the watermarked medical image is 97.4129 to 104.0503 on the PSNR scale for three different medical images by DWT, while the BER range from 0.0038 to 0.0146 respectively. While under DCT, the performance under PSNR for three different medical images are 40.97, 43.15, 11.89 respectively, and BER of 0.39, 0.2995, 0.4259 respectively. This finding indicates that DWT outperformed DCT on PSNR.
Multilevel Thresholding Using Black Widow Optimization

The key objective of any multilevel thresholding method is to obtain the optimal thresholds on a target image for the region of interest. Traditional methods of multilevel thresholding suffer from poor accuracy and computationally expensive. Optimization algorithms are found suitable to answer these difficulties effectively. In this paper, we suggest a new multilevel thresholding algorithm using Black Widow Optimization (BWO). The paper uses the unique mating behavior concept of the black widow spider to optimize six different objective functions: Otsu's between-class variance, Tsallis entropy, Masi entropy, Kapur's entropy, Renyi's entropy, and fuzzy entropy for obtaining optimal threshold values. The performance of the proposed thresholding method is evaluated by generating the segmented images of a set of standard grayscale and color images using well-known performance metrics such as PSNR, SSIM, and FSIM. The experimental outcomes show a significant improvement in the performance of the proposed method in segmenting both grayscale and color images.
Bayesian decision rules to classification problems

Summary In this paper, we analysed classification rules under Bayesian decision theory. The setup we considered here is fairly general, which can represent all possible parametric models. The Bayes classification rule we investigated minimises the Bayes risk under general loss functions. Among the existing literatures, the 0‐1 loss function appears most frequently, under which the Bayes classification rule is determined by the posterior predictive densities. Theoretically, we extended the Bernstein–von Mises theorem to the multiple‐sample case. On this basis, the oracle property of Bayes classification rule has been discussed in detail, which refers to the convergence of the Bayes classification rule to the one built from the true distributions, as the sample size tends to infinity. Simulations show that the Bayes classification rules do have some advantages over the traditional classifiers, especially when the number of features approaches the sample size.
Kernel based Dirichlet sequences

Let X=(X1,X2,…) be a sequence of random variables with values in a standard space (S,B). Suppose X1∼νandP(Xn+1∈⋅∣X1,…,Xn)=θν(⋅)+ ∑i=1nK(Xi)(⋅) n+θa.s. where θ>0 is a constant, ν a probability measure on B, and K a random probability measure on B. Then, X is exchangeable whenever K is a regular conditional distribution for ν given any sub-σ-field of B. Under this assumption, X enjoys all the main properties of classical Dirichlet sequences, including Sethuraman’s representation, conjugacy property, and convergence in total variation of predictive distributions. If μ is the weak limit of the empirical measures, conditions for μ to be a.s. discrete, or a.s. non-atomic, or μ≪ν a.s., are provided. Two CLT’s are proved as well. The first deals with stable convergence while the second concerns total variation distance.
A Machine Learning Approach for Predicting Human Preference for Graph Layouts

Understanding what graph layout human prefer and why they prefer such graph layout is significant and challenging due to the highly complex visual perception and cognition system in human brain. In this paper, we present the first machine learning approach for predicting human preference for graph layouts.In general, the data sets with human preference labels are limited and insufficient for training deep networks. To address this, we train our deep learning model by employing the transfer learning method, e.g., exploiting the quality metrics, such as shape-based metrics, edge crossing and stress, which are shown to be correlated to human preference on graph layouts. Experimental results using the ground truth human preference data sets show that our model can successfully predict human preference for graph layouts. To our best knowledge, this is the first approach for predicting qualitative evaluation of graph layouts using human preference experiment data.
Blockchain applications within the public sector: evidence from an international census

The attention around blockchain in the public sphere is constantly increasing together with the expectations around its potentialities. Existing studies are mainly focused on technical elements or on a conceptual level of analysis. However, blockchain applications are starting to appear and be adopted by public organizations. Hence is now time for scholars to make a step forward, i.e moving from a theoretical level to a practical one, observing, studying, and analysing concrete existing solutions. This exploratory study aims at supporting scholars in doing this step. With a systematic analysis of secondary sources, it maps the current ecosystem of blockchain applications within the public sector, to identify the state-of-the-art worldwide and the main trends and features that characterize blockchain implementation.
Click models inspired learning to rank

Purpose Incorporating users’ behavior patterns could help in the ranking process. Different click models (CMs) are introduced to model the sophisticated search-time behavior of users among which commonly used the triple of attractiveness, examination and satisfaction. Inspired by this fact and considering the psychological definitions of these concepts, this paper aims to propose a novel learning to rank by redefining these concepts. The attractiveness and examination factors could be calculated using a limited subset of information retrieval (IR) features by the random forest algorithm, and then they are combined with each other to predicate the satisfaction factor which is considered as the relevance level. Design/methodology/approach The attractiveness and examination factors of a given document are usually considered as its perceived relevance and the fast scan of its snippet, respectively. Here, attractiveness and examination factors are regarded as the click-count and the investigation rate, respectively. Also, the satisfaction of a document is supposed to be the same as its relevance level for a given query. This idea is supported by the strong correlation between attractiveness-satisfaction and the examination-satisfaction. Applying random forest algorithm, the attractiveness and examination factors are calculated using a very limited set of the primitive features of query-document pairs. Then, by using the ordered weighted averaging operator, these factors are aggregated to estimate the satisfaction. Findings Experimental results on MSLR-WEB10K and WCL2R data sets show the superiority of this algorithm over the state-of-the-art ranking algorithms in terms of P@n and NDCG criteria. The enhancement is more noticeable in top-ranked items which are reviewed more by the users. Originality/value This paper proposes a novel learning to rank based on the redefinition of major building blocks of the CMs which are the attractiveness, examination and satisfactory. It proposes a method to use a very limited number of selected IR features to estimate the attractiveness and examination factors and then combines these factors to predicate the satisfactory which is regarded as the relevance level of a document with respect to a given query.
Multitask Deep Learning for Edge Intelligence Video Surveillance System

From the mutual empowerment of two high-speed development technologies: artificial intelligence and edge computing, we propose a tailored Edge Intelligent Video Surveillance (EIVS) system. It is a scalable edge computing architecture and uses multitask deep learning for relevant computer vision tasks. Due to the potential application of different surveillance devices are widely different, we adopt a smart IoT module to normalize the video data of different cameras, thus the EIVS system can conveniently found proper data for a specific task. In addition, the deep learning models can be deployed at every EIVS nodes, to make computer vision tasks on the normalized data. Meanwhile, due to the training and deploying of deep learning model are usually separated, for the related tasks in the same scenario, we propose to collaboratively train the depth learning models in a multitask paradigm on the cloud server. The simulation results on the publicly available datasets show that the system continuously supports intelligent monitoring tasks, has good scalability, and can improve performance through multitask learning.
Variational Inference for Latent Space Models for Dynamic Networks

Latent space models are popular for analyzing dynamic network data. We propose a variational approach to estimate the model parameters as well as the latent positions of the nodes in the network. The variational approach is much faster than Markov chain Monte Carlo algorithms, and is able to handle large networks. Theoretical properties of the variational Bayes risk of the proposed procedure are provided. We apply the variational method and latent space model to simulated data as well as real data to demonstrate its performance.
THE IMPACT OF ARTIFICIAL NEURAL NETWORK’S STRUCTURE ON ITS EFFICIENCY FOR FINANCIAL INDICATORS FORECASTING

The article investigates the influence of artificial neural network’s structure on the results, with example of multlayer perceptron for forecasting some of the financial indicators. Multiple tests were made with various networks structures: different numbers of hidden layers and different numbers of neurons in these layers. Based on tests results, the increase of network’s size is effective to a certain extent, but at some point the further size increase is unreasonable. Also, the test results demonstrate that overfitting problem for multilayer perceptron is not as crucial as for the other machine learning models, such as regression. Key words: artificial neural networks, forecasting, multlayer perceptron, overfitting, artificial neural netwok’s size.
Intelligent Course Scheduling System Based on Case-based Reasoning

Abstract In view of the current situation of tight teaching resources, the intelligent scheduling system alleviates this problem to a certain extent, and the advantages and disadvantages of the scheduling method determine the performance of the scheduling system. a method based on Case-based reasoning is presented in this paper to solve the course scheduling problem. Using graph theory, a case representation model is built for the course scheduling problem, a measure is established for case similarity, and a case retrieval method based on maximum common subgraph selection is brought forward. Finally, the validity of the proposed method for solving course scheduling problem based on case-based reasoning is verified through an experiment.
Analysis of Students Behavior Characteristics Based on K-mediods + Eclat

Aiming at the problems of imperfect information management platform and Low quality of excavation, a combined algorithm of student behavior analysis based on clustering and association rules algorithm is proposed. Firstly, K-mediods algorithm is used to cluster the student behavior data, and the clustering results are discretized. Then, the association between student behavior and performance is analyzed by Eclat algorithm to extract important rules. Finally, combined with the results of the two algorithms, the behavior factors affecting students&#x0027; performance are analyzed comprehensively. The analysis results show that the use of the above-mentioned combined algorithm not only improves the quality of the mining results, but also provides guidance and suggestions to teachers&#x0027; teaching work and students&#x0027; learning conditions through the mining results.
An End-to-end Hierarchical Multi-task Learning Framework of Sentiment Analysis and Key Entity Identification for Online Financial Texts

Timely and accurate collection and analysis of a financial entity's online text not only helps investors a void risks and optimize investment plans, but also helps the institutions's own public opinion management. However, how to automatically identify the negative text from a large number of online financial text and identify the key entities mainly described by the text is a difficult problem. Most existing studies focuses only on the sentimental polarity of financial texts or only on all the entities that extract them. However, investors and institution may be more focused on key entities in financial texts, rather than just the sentiment of financial texts. Besides, the sentimental polarity of online news is often closely related to key entities which fits well with the idea of multi-task learning. In this paper, we propose a hierarchical multi-task learning framework(HMFF) to not only identify the sentimental polarity of online news but also enhance the feature learning of key entity identification. Experimental results show that our framework not only end-to-end identifies the sentimental polarity of financial text and key entities, but also improves the accuracy of the model.
The application of AI in the aesthetic cultivation of Chinese education

This article will briefly analyse the research background and research significance of the infiltration of aesthetic cultivation in Chinese language and literature education in the context of new media. And through the calculation based on decision tree and C4.5 algorithm, the paper tries to makes the construction of infiltration system of aesthetic cultivation in Chinese language and literature education in the context of new media more scientific and reasonable. This paper also analyses the main connotation and significance of aesthetic cultivation and puts forward the effective infiltration way of aesthetic cultivation in Chinese language and literature in the context of new media, aiming at promoting the comprehensive and coordinated development of Chinese students.
Estimating Costs for Adopting and Using Model-Based Testing in Agile SCRUM Teams

The introduction of Model-Based testing (MBT) in software development teams introduces change in software testing, as traditional testing processes are replaced by MBT. When planning for MBT adoption, team leaders can benefit from tools that are able to provide information on the cost of introducing MBT in their teams. We have developed and present a set of models, which, given a number of initial parameters such as employee competence and availability, as well as historic usage of data, can estimate costs for MBT adoption. These models are designed based on experience from previous practicing of MBT. To demonstrate the practical value of our models, we run a number of simulations on hypothetical MBT adoption and use scenarios, which can be realistically applied to different teams considering adopting MBT.
Sustainable Peace Building and Development in Nigeria’s Post-Amnesty Programme: The Role of Corporate Social Responsibility in Oil Host Communities

The activities and violence of militants in the Niger Delta which saw the capacity for production of oil in Nigeria fall to an all-time low resulted in the federal government of Nigeria (FGN) announcing the Presidential Amnesty Programme in return for peace in the region. We examine how multinational oil companies’ (MOCs’) corporate social responsibility (CSR) impact on entrepreneurship development and job creation to absorb the youths. 1200 youths were sampled across the nine states of Niger Delta. Results from the use of estimated logit model reveal that GMoU interventions are prevalent in communities with greater ownership, creating room for better projects, sustainability and improved trust; yet the interventions failed to make significant impact on entrepreneurship development and job creation. Clearly, facilitating how youths get involved in skill acquisition and empowerment programmes would help them become entrepreneurs, improving their self-assurance that they can prosper outside militancy activities and violence.
Data page classification in holographic memory using binary neural network

This study investigates the performance of a binary neural network, which is a lightweight neural network, for classification problems in holographic applications. We performed data classification in holographic memory using XNOR-Net as one of the binary neural networks. We compared the performance of the binary neural network with convolutional neural networks.
Feature Aggregation Network with Tri-Hybrid Loss for Instance Segmentation

Limited by the size of feature maps in the mask head, the operation of simply stacking convolutional blocks in previous instance segmentation methods cannot obtain comprehensive features. In contrast, we design a Feature Aggregation Net-work (FAN) where three separate modules are respectively responsible for extracting salient features from the dimensions of channel, space, and scale. Then, these salient features are further aggregated based on their affinities generated by an Affinity Computation Module (ACM) with the original features. In this way, features become more distinct, which is conducive to the subsequent mask prediction. To further improve the segmentation performance of hard examples with-out introducing extra inference overhead, we also propose a novel loss function named Tri-hybrid loss where the optimization can be simultaneously performed from the perspectives of pixel, boundary, and instance. With these improvements, our model outperforms state-of-the-arts with 39.5 mask AP on COCO test-dev2017.
Image steganography algorithm based on image colorization

Aiming at the problems of stego images with modification traces and unnatural image generation in the image steganography algorithm based on deep learning, this paper proposes an image steganography algorithm based on image coloring. It first embeds secret information in a gray image using traditional image steganography algorithms, and then uses image coloring algorithms to color the gray image, disguising the modification of the gray image as the result of image embedding, highlighting the coloring features of the algorithm, reducing the steganographic characteristics of the algorithm, and guide the enemy to mistake the algorithm for the image coloring algorithm, thereby reducing the probability of steganography being discovered. Experimental results show that this algorithm has good results in steganographic capacity and generated images.
Hydraulic lifting technology of steel grid roof of a hangar

Abstract The grid structure is more and more widely used in the civil engineering industry, but due to the large number of rods and complex installation, it is extremely difficult to construct at high altitude. In order to overcome this problem, engineers proposed a hydraulic jacking method. The mechanical properties of steel structures are more complicated, especially in the construction process, the force situation and the design load is quite different. In order to explore the reliability of the hydraulic lifting process of the steel grid, this article takes the hydraulic lifting technology of the steel grid roof of a hangar as the engineering background. This project uses Sap2000 finite element analysis software to model, simulate the construction process of the jacking grid, analyze the mechanical characteristics and stability of the grid during the jacking process, and use it to guide the construction. The results show that: for large grid structures constructed at high altitudes and hydraulic jacking technology can effectively improve the construction efficiency and safety.
Comparison of deep learning-based methods in multimodal anomaly detection: A case study in human–robot collaboration

Human-Robot Collaboration (HRC) has been widely used in daily life and industry for maximizing the advantages of humans and robots, respectively. However, the internal modeling errors or external perturbations still affect robotic systems such as human collisions and environmental changes. Multimodal anomaly detection plays an increasingly important role in HRC applications, which detects unexpected anomalies from multimodal signals. Due to the complex temporal dependence and stochasticity, it is still difficult to choose a common model applicable to all collaborative tasks, and lack of comparative analysis of existing methods and verification of specific application cases. In this paper, six representative deep learning-based methods are evaluated and the comparing metrics including detection accuracy, multi-modality combinations, and anomaly time bias. For a fair comparison, each detector models multimodal signals from non-anomalous samples and then determines an anomaly using a predefined threshold. We evaluate the detectors with force, torque, velocity, tactile, and kinematic sensing during a human-robot kitting experiment that consists of six individual skills, results indicate that the LSTM-DAGMM based detector outperformed the others, which yielding higher accuracy and efficiency. The metrics are measured with the RUC and ROC by changing the settings of multi-modality combinations and various anomaly biases, which aim to obtain the best performance of multimodal anomaly detection.
Face, Expression and Gesture Recognition and Compilation in Database Using Machine Learning

In today's scenario, numbers of crimes have increased day by day. At many public places government has placed many CCTV cameras so police can get that CCTV footage to identify the suspects but sometimes it becomes difficult to recognize the criminals So here we have come up with a solution to make this process smooth, easier than the traditional one. The system which automates all the suspect recognition process and provides better solutions to reduce the increasing rate of crimes. We plan to design a system to capture face, expressions and gestures of the targeted people (Criminals) through distributed CCTV System and are maintaining it in a database along with time and location stamp. The compiled database will be used to identify suspects from video clips of crime related CCTV footage captured series of CCTV Systems located on routes and close to scene of crime. This research discusses the various types of methodologies that can be used to identify the suspects which are captured in CCTV footage and convert it into useful information for further analysis of particular crime cases.