Reviewerly/exHarmony huggingface dataset:
3D Image Representation Using Separable Discrete Orthogonal Moments

This paper introduces a new sets of separable three-dimensional moments, called Racah-Tchebichef-Krawtchouk Moments (RTKM), Racah-Krawtchouk-Krawtchouk Moments (RKKM) and Racah-Racah-Krawtchouk Moments (RRKM), for the field of 3D image representation. First, we will provide the detailed mathematical framework of the proposed separable 3D moments. Second, we outline the main applications of the newly introduced moments. Where, the global feature extraction capability is evaluated by measuring reconstruction quality of 3D image, under noisy and noise-free conditions. While, the ability to extract local feature is also demonstrated by adjusting the polynomials parameters value according to the desired Region of Interest (ROI). The obtained results show that the proposed separable moments perform better than the classical moments in terms of image representation capability.
Cross domain association using transfer subspace learning

Transfer learning has gained more attention recently by utilizing knowledge acquired from one domain to advance a learning performance in another domain. Existing homogeneous transfer learning methods have progressed to a point where feature spaces are common in training and testing domains. However, heterogeneous transfer learning is still in its nascent stage where features of training and testing domains are different. Taking this into account, Bregman Divergence Regularization is used to minimize the probability distribution difference between training and testing domains and to take them together to a shared subspace. To discriminate data within individual domains, a projection matrix is obtained using Fisher Linear Discriminant Analysis subspace learning algorithm. Experimentation is performed on two efficiently used biometrics: the face and fingerprint. Two types of cross-domain settings are used: (1) Face + Finger2Finger where training samples come from face (labeled samples) and fingerprint (unlabeled samples) data sets, while testing is performed on a fingerprint dataset. (2) Finger + Face2Face where training samples come from fingerprint (labeled samples) and face (unlabeled samples) data sets while testing is performed on a face dataset. This paper proposes a cross domain association between face and fingerprint that finds utility in forensic applications.
Time Series Analysis and Forecasting of Oilseeds Production in India: Using Autoregressive Integrated Moving Average and Group Method of Data Handling – Neural Network

Oilseeds have been the backbone of India’s agricultural economy since long. Oilseed crops play the second most important role in Indian agricultural economy, next to food grains, in terms of area and production. Oilseeds production in India has increased with time, however, the increasing demand for edible oils necessitated the imports in large quantities, leading to a substantial drain of foreign exchange. The need for addressing this deficit motivated a systematic study of the oilseeds economy to formulate appropriate strategies to bridge the demand-supply gap. In this study, an effort is made to forecast oilseeds production by using Autoregressive Integrated Moving Average (ARIMA) model, which is the most widely used model for forecasting time series. One of the main drawbacks of this model is the presumption of linearity. The Group Method of Data Handling (GMDH) model has also been applied for forecasting the oilseeds production because it contains nonlinear patterns. Both ARIMA and GMDH are mathematical models well-known for time series forecasting. The results obtained by the GMDH are compared with the results of ARIMA model. The comparison of modeling results shows that the GMDH model perform better than the ARIMA model in terms of mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The experimental results of both models indicate that the GMDH model is a powerful tool to handle the time series data and it provides a promising technique in time series forecasting methods.
The research of the application of the binary search algorithm of RFID system in the supermarket shopping information identification

The traditional bar code scanning in supermarkets is inefficient, which has been unable to meet the further development of a supermarket. To meet the needs of quick shopping in supermarkets, binary search algorithm has been proposed to prevent from anti-collision, which makes it faster to scan and identify the goods. On this basis, the goods which have not been checked out will be scanned again to obtain security. Besides, it plays an important role in the supermarket shopping system to achieve the functions of self-purchase and membership scheme. For the RFID supermarket shopping system, the test of the checkout efficiency, product recognition rate, and security were done. The result showed that the binary search algorithm had good accuracy in the RFID supermarket shopping system. The result also proved the rationality of the RFID supermarket shopping system, which can meet the daily needs of supermarkets. The method was applying the emerging technology into the traditional supermarket shopping system, which improved the profitability and management level of supermarkets.
Evolved Fuzzy NN Control for Discrete-Time Nonlinear Systems

To guarantee the asymptotic stability of discrete-time nonlinear systems, this paper proposes an Evolved Bat Algorithm (EBA) fuzzy neural network (NN) controller. In the evolved fuzzy NN modeling, an NN model and linear differential inclusion (LDI) representation are established for arbitrary nonlinear dynamics. This representation is constructed by the use of sector nonlinearity to convert a nonlinear model to the multiple rule base of the linear model, and a new sufficiency condition to guarantee asymptotic stability in the Lyapunov function is implemented in terms of linear matrix inequalities. The proposed method is an enhancement of existing methods which produces good results.
Missed cancer and visual search of mammograms: what feature based machine-learning can tell us that deep-convolution learning cannot

Significant amount of effort has been invested in improving the quality of breast imaging modalities (for example, mammography) to increase the accuracy of breast cancer detection. Despite that, about 4-34% of cancers are still missed during mammographic examination of cancer of the breast. This indicates the need to explore a) The features of the lesions that are missed, and b) Whether the features of missed cancers contribute to why some cancers are not ‘looked at’ (search error) whereas others are ‘looked at’ but still not reported. In this visual search study, we perform feature analysis of all lesions that were missed by at least one participating radiologist. We focus on features extracted by means of Grey Level Co-occurrence Matrix properties, textural properties using Gabor filters, statistical information extraction using 2<sup>nd</sup> and higher-order (3<sup>rd</sup> and 4<sup>th</sup>) spectral analysis and also spatial-temporal attributes of radiologists’ visual search behaviour. We perform Analysis of Variance (ANOVA) on these features to explore the differences in features for cancers that were missed due to a) search, b) perception and c) decision making errors. Using these features, we trained Support Vector Machine, Gradient Boosting and stochastic gradient decent classifiers to determine the type of missed cancer (search, perception and decision making). We compared these feature-based models with a model trained using deep convolution neural network that learns features by itself. We determined whether deep learning or traditional machine learning performs best in this task.
Weighting Quantization Matrices for HEVC/H.265-Coded RGB Videos

In the HEVC/H.265 video coding standard, weighting quantization matrices (WQMs) are supported to take advantage of the characteristics of the human visual system (HVS). However, the default WQMs utilized in HEVC are developed for YCbCr videos instead of RGB videos. In this paper, a set of new WQMs is proposed for video coding in RGB color space. First, we utilize the spatial contrast sensitivity function (CSF) to model the bandpass property of HVS. To derive the parameters of the spatial CSF, a series of subjective experiments is conducted to obtain the just-noticeable distortion (JND) thresholds of several selected DCT subbands. In addition, the sensitivities of different DCT subbands in one color channel, as well as among R, G, and B channels, are considered to design the WQMs of intra-coded 8 × 8 blocks. Moreover, to reduce the data size of WQMs, the WQMs for other block sizes are derived from intra 8 × 8 WQMs. The proposed WQMs are then applied into HEVC to directly code RGB videos. The experimental results demonstrate that when the PSNRs of G, B, and R channels are combined with a ratio of 4:1:1, the proposed WQMs can achieve an average BD-rate saving of 12.64% and 20.51%, respectively, in all-intra (AI) and low-delay (LD) profiles compared to HEVC without WQMs. The proposed scheme also enjoys a better video quality metric (VQM) performance.
ByNowLife: A Novel Framework for OWL and Bayesian Network Integration

An ontology-based system can currently logically reason through the Web Ontology Language Description Logic (OWL DL). To perform probabilistic reasoning, the system must use a separate knowledge base, separate processing, or third-party applications. Previous studies mainly focus on how to represent probabilistic information in ontologies and perform reasoning through them. These approaches are not suitable for systems that already have running ontologies and Bayesian network (BN) knowledge bases because users must rewrite the probabilistic information contained in a BN into an ontology. We present a framework called ByNowLife, which is a novel approach for integrating BN with OWL by providing an interface for retrieving probabilistic information through SPARQL queries. ByNowLife catalyzes the integration process by transforming logical information contained in an ontology into a BN and probabilistic information contained in a BN into an ontology. This produces a system with a complete knowledge base. Using ByNowLife, a system that already has separate ontologies and BN knowledge bases can integrate them into a single knowledge base and perform both logical and probabilistic reasoning through it. The integration not only facilitates the unity of reasoning but also has several other advantages, such as ontology enrichment and BN structural adjustment through structural and parameter learning.
Towards functional change decision support based on COSMIC FSM method

Abstract Context: Managing requirements change is a central issue in the software development industry. In fact, inappropriate decisions about a change request may jeopardize the project development progress by going over budget/time or delivering a software with functional requirements that do not fully meet the user’s needs. Hence, a change decision support is required for the success of the software development. Objective: This paper has a three-fold objective: (i) explore the applicability of the ISO standard COSMIC FSM method to evaluate a change request; (ii) investigate the use of estimation models to predict the effort required to handle a functional change and its impact on the initially estimated software development effort; and (iii) propose a decision support method that offers the appropriate information for the change advisory board members to decide whether to accept, deny or defer a functional change request. Method: To guide the decision on a change request, the method proposed in this paper accounts for the most important factors when evaluating a change request, namely the functional change status, the preference of the change requester, and the effort required to handle the change. The functional change status is identified based on the sensitivity of the changed functionality and the functional size of the functional change. The functional change effort can be estimated using several ways including the COCOMO II model, the Simple Linear Regression Model and expert judgment. Furthermore, this paper proposes a prototype to determine automatically the functional change status and offers pertinent information that the change advisory board can use to determine how to handle a change request. The use of the decision support method and tool is illustrated through three case studies. Results: A decision support method to help decision-makers respond to a functional change request is provided. This method takes into account the functional change status, the preference of the change requester and the functional change effort. The empirical evaluation of the proposed method is illustrated through three case studies. The role of experiments here is primarily to provide a proof-of-concept rather than an exhaustive evaluation. Conclusion: Using COSMIC FSM method, it is possible to identify functional changes leading to a potential impact on the software development progress. Based on the evaluation of the functional change, the change advisory board members can make judicious decisions about whether to accept, defer or deny a functional change request.
Detecting Anomalies by using Self-Organizing Maps in Industrial Environments

Detecting anomalies caused by intruders are a big challenge in industrial environments due to the complex environmental interdependencies and proprietary fieldbus protocols. In this paper, we proposed a network-based method for detecting anomalies by using unsupervised artificial neural networks called Self-Organizing Maps (SOMs). Therefore, we published an algorithm which identifies clusters and cluster centroids in SOMs to gain knowledge about the underlying data structure. In the training phase we created two neural networks, one for clustering the network data and the other one for finding the cluster centroids. In the operating phase our approach is able to detect anomalies by comparing new data samples with the first trained SOM model. We used a confidence interval to decide if the sample is too far from its best matching unit. A novel additional confidence interval for the second SOM is proposed to minimize false positives which have been a major drawback of machine learning methods in anomaly detection. We implemented our approach in a robot cell and infiltrated the network like an intruder would do to evaluate our method. As a result, we significantly reduced the false positive rate to 0.07% using the second interval while providing an accuracy of 99% for the detection of network attacks.
Enhanced Chemical Structure Recognition and Prediction Using Bayesian Fusion

In this paper we present a novel system for chemical symbol recognition capable of recognizing and predicting handwritten chemical structures. While the system is aimed primarily at chemists and search engine developers, it is also intended to be used as a learning tool in chemistry education on a student's touch screen computer or in classroom using smart boards. As a student draws on a device screen, the system would provide instantaneous feedback concerning possible chemical structures to complete the drawing, providing a continuously updated table of closely matching candidate symbols, and automatically identifying parts of the molecule that may be combined with other chemical structures. Major building blocks of the suggested system are two Bayesian-based image recognizers: one for collecting input images and one for producing the final output. Between input and output sketch classification is done using state of the art methods. By jointly incorporating feature-based and image-based techniques the system is able to recognize messy artifact-infected sketches and to predict possible terminations. Various performance related experiments proved the system to be considerably faster and more accurate especially when compared to neural-network based techniques.
Syntactic Graph Convolution in Multi-Task Learning for Identifying and Classifying the Argument Component

In this study, we focus on conducting fundamental research for combining syntactic knowledge with neural studies that utilize syntactic information in the argument component identification and classification (AC-I/C) tasks in argument mining. Our approach examines the role of syntactic graph convolutional networks (GCNs) used in multi-task learning for AC-I/C. Our contributions are as follows: 1) we propose a method to incorporate the dependency-based syntactic GCN into multitask learning models for AC-I/C tasks; 2) we demonstrate the effectiveness of the proposed syntactic GCN through experiments involving a few renowned argument mining datasets. The experimental results demonstrate that syntactic GCNs are effective when applied to specific datasets. Furthermore, we observe that the proposed syntactic GCN is promising for a lexically independent scenario. Additionally, our experimental code is available for reproducibility.11https://github.com/EdoFrank/GCNArgMining
A Robust Blind Video Watermarking Scheme based on Discrete Wavelet Transform and Singular Value Decomposition

The outgrowth in technological world has massively promoted to information fraud and misappropriation by the ease of multimedia data content regeneration and modification. Consequently, security of digital media is considered among the biggest issues in multimedia services. Watermarking, consisting in hiding a signature known as watermark in a host signal, is one of the potential solutions used purposely for media security and authentication. In this paper, we propose a robust video watermarking scheme using Discrete Wavelet Transform and Singular Value Decomposition. We embed the watermark into the mid frequency sub-bands based on an additive method. The extraction process operates following a blind detection algorithm. Several attacks are applied and different performance metrics are computed to assess the robustness and the imperceptibility of the proposed watermarking. The results reveal that the proposed scheme is robust against different attacks and achieves a good level of imperceptibility.
A Hybrid Model of Crime Prediction

According to statistics, crimes are not random in spatial and temporal distribution. The key to predicting policing is predicting in advance when and where a crime may occur, and providing a reference for preventive measures of police officers. In this paper, a hybrid model of LSTM and STARMA is established. Crime data is complicated. It can be decomposed into trend components, seasonal components, and random components. The LSTM model is established for the trend components and the seasonal components . The STARMA model is established about random components. It solves the problem that the STARMA cannot be modeled on nonlinear or non-stationary data. Verification results show that the model is effective in crime prediction.
Low-Dimensional Density Ratio Estimation for Covariate Shift Correction.

Covariate shift is a prevalent setting for supervised learning in the wild when the training and test data are drawn from different time periods, different but related domains, or via different sampling strategies. This paper addresses a transfer learning setting, with covariate shift between source and target domains. Most existing methods for correcting covariate shift exploit density ratios of the features to reweight the source-domain data, and when the features are high-dimensional, the estimated density ratios may suffer large estimation variances, leading to poor prediction performance. In this work, we investigate the dependence of covariate shift correction performance on the dimensionality of the features, and propose a correction method that finds a low-dimensional representation of the features, which takes into account feature relevant to the target Y, and exploits the density ratio of this representation for importance reweighting. We discuss the factors affecting the performance of our method and demonstrate its capabilities on both pseudo-real and real-world data.
Region-Based Active Learning

We study a scenario of active learning where the input space is partitioned into different regions and where a distinct hypothesis is learned for each region. We first introduce a new active learning algorithm (EIWAL), which is an enhanced version of the IWAL algorithm, based on a finer analysis that results in more favorable learning guarantees. Then, we present a new learning algorithm for region-based active learning, ORIWAL, in which either IWAL or EIWAL serve as a subroutine. ORIWAL optimally allocates points to the subroutine algorithm for each region. We give a detailed theoretical analysis of ORIWAL, including generalization error guarantees and bounds on the number of points labeled, in terms of both the hypothesis set used in each region and the probability mass of that region. We also report the results of several experiments for our algorithm which demonstrate substantial benefits over existing non-region-based active learning algorithms, such as IWAL, and over passive learning.
Image Encryption with The Cross Diffusion of Two Chaotic Maps

Information security has become increasingly important with the rapid development of mobile devices and internet.An efficient encryption system is a key to this end.In this paper, we propose an image encryption method based on the cross diffusion of two chaotic maps.We use two chaotic sequences, namely the Logistic map and the Chebyshev map, for key generation which has larger security key space than single one.Moreover, we use these two sequences for further image encryption diffusion which decreases the correlation of neighboring pixels significantly.We conduct extensive experiments on several well-known images like Lena, Baboon, Koala, etc. Experimental results show that our algorithm has the characteristics of large key space, fast, robust to statistic attack, etc.
Towards Computational Assessment of Idea Novelty

In crowdsourcing ideation websites, companies can easily collect large amount of ideas.Screening through such volume of ideas is very costly and challenging, necessitating automatic approaches.It would be particularly useful to automatically evaluate idea novelty since companies commonly seek novel ideas.Three computational approaches were tested, based on Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA) and term frequency-inverse document frequency (TF-IDF), respectively.These three approaches were used on three set of ideas and the computed idea novelty was compared with human expert evaluation.TF-IDF based measure correlated better with expert evaluation than the other two measures.However, our results show that these approaches do not match human judgement well enough to replace it.