Reviewerly/exHarmony huggingface dataset:
A New Data Hiding Scheme Using Laplace Transformation in Frequency Domain Steganography

Today, in the age of wireless communication, network security is an important issue. Image steganography is a popular approach to make communication secure. Among two categories (spatial domain and frequency domain) of image steganography, frequency domain is more secured than the spatial domain. The proposed scheme develops a secret communication technique in frequency domain steganography. In steganography, the concept of Laplace transformation is used during embedding and extraction procedures of secret information in image files. As a result, all the conditions of information security are developed in high density. The efficiency and effectiveness of the scheme is evaluated by measuring the hacking chance from different statistical attacks by RS steganalysis and originality of actual cover image after inserting secret information by Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR).
Joint variational Bayesian based localization estimation algorithm on distributed gas source sensor network

According to the non-linear and unknown diffusion distribution model characteristics of gas leakage diffusion in the real environment, a distributed joint optimal estimation algorithm for gas source localization and parameters estimation was proposed in sensor networks. Firstly, the gas source localization framework was constructed based on the compressed sensing theory, Secondly, the joint sparse estimation method of the state distribution and unknown parameters of gas diffusive source is proposed based on the variational Bayesian inference algorithm. In which, an adaptive grid division strategy is given to improve the accuracy and performance of the joint estimation method and to balance the relationship between energy consumption and network resources. Finally, simulation results show that the proposed algorithm could effectively achieve a joint estimation of the diffusive source state and parameters, which could achieve higher estimation accuracy in a shorter time and meet the real-time requirements in complex environment compared with the traditional CS method.
Interactive Skill Based Labor Market Mechanics and Dynamics Analysis System Using Machine Learning and Big Data

Interest in talent recognition, talent recruitment, education and labor mobility has been on the rise in last years. Business sector is changing its human resources (HR) policies globally, changing ways in policies and practices related to employee management and employer branding. The process of talent recognition and/or search was mostly manual work, often carried by professional agencies or HR officer in organization. In today’s challenging labor market environment, this process is inefficient and slow with and often limited in success. In this paper we are focused on skills, education and lifelong learning domain which has an important role in the 10 priorities of the European Commission (EC) 2014–2019. Our starting point was to look for machine learning and big data techniques to support the policy makers and analysts in reducing mismatch between jobs and skills at regional level in the European Union (EU) through the use of data. Our research includes massive semi structured resume dataset (50+ million documents) combined with several official statistical surveys. We were able to leverage the advances in machine learning and big data to automate resume/skill classification and to improve productivity in skill-based labor market mechanics and dynamics analysis. This paper proposes a model of extracting important information after resumes are being partially classified, classify skills using European multilingual classification of Skills, Competences, Qualifications and Occupations (ESCO) by skill pillar matching and use modern interactive visualization tools to gain smart insights powered by geospatial and time-series analysis. Research goals are to point attention towards data science and machine learning and its usage in labor and educational market mechanics and dynamics.
Residual Mask Based on MobileNet-V2 for Driver's Dangerous Behavior Recognition

We introduce a new residual mask into the inverted residual structure in MobileNet-V2, which significantly improved the performance of the original network, with only a minimal number of parameters added. We train our networks on a larger public dataset to detect distract behaviors of a driver. Experiments show that MobileNet-v2 with the proposed residual mask converges faster and achieves better accuracy than the original network.
Towards Elasticity for SME Cloud Services

High availability and performance are metrics that directly impact the Quality of Experience of cloud service customers. On these environments, several models of elasticity have been used to meet seasonal demands, from the acquisition of additional resources to the expansion of private clouds to hybrid clouds. However, both scenarios are burdensome for Small and Medium sized Enterprises (SMEs). In this article, we present a strategy for elasticity management using existing and idle resources from the enterprise itself, which may be underutilized precisely during the period of applications' higher demand. In order to evaluate our solution, tests were carried out in the CloudSim simulator using typical scale-out applications on a dynamic cloud infrastructure, taking into account response time as a performance metric. Besides the empirical experiments, we also analyzed the scalability of our approach with the CloudSim simulator. Results of our evaluations demonstrated that our strategy is able to improve applications performance in up to 75%.
Cloud Database Encryption Technology Based on Combinatorial Encryption

Traditional cloud database directly stores user plaintext information, information security is directly related to the security of Cloud server, which will create a great security risk. The user's information security is not guaranteed. In this paper, a database encryption technology is designed, which can balance the problem of information security and use efficiency well. In this paper, a simplified onion encryption model is designed and implemented, which can realize the full homomorphic encryption on the cloud database to a certain extent, and improve the efficiency of ciphertext operation to a certain extent.
Image Representation for Cognitive Systems Using SOEKS and DDNA: A Case Study for PPE Compliance

Cognitive Vision Systems have gained significant interest from academia and industry during the past few decade, and one of the main reasons behind this is the potential of such technologies to revolutionize human life as they intend to work under complex visual scenes, adapting to a comprehensive range of unforeseen changes, and exhibiting prospective behavior. The combination of these properties aims to mimic the human capabilities and create more intelligent and efficient environments. Nevertheless, preserving the environment such as humans do still remains a challenge in cognitive systems applications due to the complexity of such process. Experts believe the starting point towards real cognitive vision systems is to establish a representation which could integrate image/video modularization and virtualization, together with information from other sources (wearable sensors, machine signals, context, etc.) and capture its knowledge. In this paper we show through a case study how Decisional DNA (DDNA), a multi-domain knowledge structure that has the Set of Experience Knowledge Structure (SOEKS) as its basis can be utilized as a comprehensive embedded knowledge representation in a Cognitive Vision System for Hazard Control (CVP-HC). The proposed application aims to ensure that workers remain safe and compliant with Health and Safety policy for use of Personal Protective Equipment (PPE) and serves as a showcase to demonstrate the representation of visual and non-visual content together as an experiential knowledge in one single structure.
Adversarial Learning of Transitive Semantic Features for Cross-Domain Recommendation

In the era of big data, recommender systems have become the key part of many Internet applications. One successful recommendation strategy is to jointly recommend items from different domains where the system can model an accurate portrait of user behaviors. However, it is still challenging to identify the correlation among various domains and make efficient utilization of features from each domain. In this paper, we propose a novel framework, called Domain Adversarial Cross-Domain Recommendation (DACDR), to learn the implicit transitive semantic features among various information relevant domains. The framework automatically retrieves semantic features from both the source and the target domains, and adaptively learns the transitive latent factors to connect the two domains. The user behaviors are then modelled by the learnt latent factors, based on which DACDR can provide an accurate recommendation. Evaluation over real-world dataset verifies that the proposed framework outperforms the state-of-the-art algorithms in terms of F1, NDCG and MRR metrics.
Data Mining and Social Network Analysis on Twitter

The emergence of a networked social structure in the last decade of twentieth century is accelerated by the evolution of information technologies and, in particular, the Internet has given rise to the full emergence of what has been called the Information Age [1] or the Information Society [2]. Social media is yet another example of people’s extraordinary ability to generate, disseminate and exchange meanings in collective interpersonal communication with a massive, real-time networked system where everything tends to be connected. The analysis of the climate of opinion on Twitter is presented around the Common Core State Standards (CCSS), one of the most ambitious educational reforms of the last 50 years in USA.
The Naïve Associative Classifier With Epsilon Disambiguation

This paper presents the Naïve Associative Classifier with Epsilon disambiguation (NACε), an extension of the Naïve Associative Classifier that, by including a procedure to disambiguate classes in regions where Bayes risk is high, has a positive effect on the performance of the classifiers of the associative approach on several datasets belonging to the financial environment, particularly in terms of credit risk. The experiments conducted to test the NACε were based on 12 datasets composed with financial information and associated with five stages of the credit process: promotion, evaluation, granting, monitoring and recovery. Due to the severe imbalance present in most datasets, the performance of the proposed algorithm was measured using the area under the ROC curve. Likewise, 5 × 2 stratified cross validation was made and finally a couple of statistical tests were applied to compare the results. After applying the NACε to the datasets, a successful disambiguation of classes was observed. In the real world this fact could help financial institutions to evaluate the credit applications more effectively and thus, contribute to the mitigation of monetary losses derived from the poor quality of the information.
Pathological Image Classification of Breast Cancer Based on Residual Network and Focal Loss

This paper proposes an improved deep residual neural network for the classification of breast cancer as either benign or malignant. Inspired by the success of using focal loss in object detection, we present a new focal loss for pathological image classification to solve the class imbalance problem encountered during training stage. Furthermore, we introduce a multi-scale acquisition structure into ResNet to get a larger range of receptive fields for each network layer and represent features at multiple scales. Data enhancement and migration learning are also used to optimize the initial parameters solving the problem of overfitting in the network. Experimental results show that our approach achieves higher accuracy of classification compared to previous methods.
Deep Convolution Neural Network Model for Indian Sign Language Classification

Communication gap between non-hearing and hearing people results in many interaction difficulties across the globe. Indian sign language is the traditional communication alternative in our country. Recently, the integration of deep learning with convolution neural network plays a major role in solving various image classification problems. The proposed deep convolution neural network model is prepared with six convolution layers and three fully connected layers by altering different parameters. This model is evaluated based on 1000 number sign images. These datasets are created in college laboratory with 100 different students. Here, 1–10 number sign images are collected, and the proposed CNN model by six convolutional layers with 1000 epochs is applied. This study highlights the training and validation accuracy analysis as well as training and validation losses. Different performance metrics are calculated to find out the accuracy of the proposed model. The aim of developing the proposed model is to find out classification accuracy for each class. This is multiclass problems as each number signs contain 100 images. This model can predict 10 different signs classes. We have taken 200 signs from 1000 number signs for testing purpose. The results indicate that the accuracy of the proposed method is rapidly increased by increasing the epochs. We have achieved 73% average classification model accuracy.
Cyber Security, IT Governance, and Performance

Cybersecurity is an emerging field with a growing body of literature and publications. It is fundamentally based in computer science and computer engineering but has recently gained popularity in business management. Despite the explosion of cybersecurity, there is a scarcity of literature on the definition of the term ‘Cybers Security' and how it is situated within different contexts. Henceforth, this chapter presents a review of the work related to cybersecurity, within different contexts, mainly IT governance and firm performance context. The work reviewed is separated into four main categories: the importance of cybersecurity and how it is measured, corporate governance and IT governance, IT governance mechanisms, and financial performance measures.
Learning to realign hierarchy for image segmentation

A hierarchical image segmentation is a set of image segmentations at different detail levels. However, objects (or even parts of the same object) may appear at different scales due to their size differences or to their distinct distances from the camera. One possible solution to cope with that is to realign the hierarchy such that every region containing an object (or its parts) is at the same level. In this work, we have explored the use of regression models to predict score values for regions belonging to a hierarchy of partitions, which are used to realign it. We have also proposed a new score calculation and a new assessment strategy considering all user-defined segmentations that exist in the ground-truth. Experimental results have pointed out that the use of new proposed score was able to improve final segmentation results.
Statistical Approach to Fuzzy Cognitive Maps

Fuzzy cognitive maps are studied from statistical standpoint. An analogy between these maps and linear regression and logistic regression models is drawn. Practical examples are also provided.
Implementation of a face recognition system as experimental practices in an artificial intelligence and pattern recognition course

Abstract In this paper, we present the methodology developed in a course of Artificial Intelligence and Pattern Recognition. The methodology is focused on building a face recognition system. For this purpose, we developed an own face recognition database named “Medfaces”. This database contains controlled and wild images for the same subjects, and it also contains infrared images and facial expression labels. This methodology has been applied for teaching telecommunications engineering course at Miguel Hernández University of Elche (Spain). We also present the results of our teaching experience in terms of student satisfaction.
Exploring 3 R’s of Long-term Tracking: Re-detection, Recovery and Reliability

Recent works have proposed several long term tracking benchmarks and highlight the importance of moving towards long-duration tracking to bridge the gap with application requirements. The current evaluation methodologies, however, do not focus on several aspects that are crucial in a long term perspective like Re-detection, Recovery, and Reliability. In this paper, we propose novel evaluation strategies for a more in-depth analysis of trackers from a long-term perspective. More specifically, (a) we test redetection capability of the trackers in the wild by simulating virtual cuts, (b) we investigate the role of chance in the recovery of tracker after failure and (c) we propose a novel metric allowing visual inference on the ability of a tracker to track contiguously (without any failure) at a given accuracy. We present several original insights derived from an extensive set of quantitative and qualitative experiments.
Analysis and a Solution of Momentarily Missed Detection for Anchor-based Object Detectors

The employment of convolutional neural networks has led to significant performance improvement on the task of object detection. However, when applying existing detectors to continuous frames in a video, we often encounter momentary miss-detection of objects, that is, objects are undetected exceptionally at a few frames, although they are correctly detected at all other frames. In this paper, we analyze the mechanism of how such miss-detection occurs. For the most popular class of detectors that are based on anchor boxes, we show the followings: i) besides apparent causes such as motion blur, occlusions, background clutters, etc., the majority of remaining miss-detection can be explained by an improper behavior of the detectors at boundaries of the anchor boxes; and ii) this can be rectified by improving the way of choosing positive samples from candidate anchor boxes when training the detectors.
Identification and characterization of hydrothermally altered minerals using surface and space-based reflectance spectroscopy, in parts of south-eastern Rajasthan, India

Imaging spectroscopy has evolved as one of the most significant advancements due to contiguous spectral coverage and higher spectral resolution which enable mineral identification and mineral exploration. Many phyllosilicate and carbonate minerals show specific spectral absorption feature in the wavelength range of visible-to-near-infra-red region of electromagnetic spectrum. These spectral features enable delineation of different mineral assemblages which in turn help in mineral prospecting using hyperspectral imaging spectra. The present study is focussed on evaluation and application of EO-1 Hyperion (hyperspectral) data as an Earth Observation tool for mineral detection and mapping in parts of Udaipur district in south-eastern Rajasthan. Hyperion reflectance imagery of this area was analysed using spectral angle mapper after pre-processing, atmospheric correction and geometric correction. Five endmembers, viz. dolomite, montmorillonite, chlorite, phlogopite and serpentine, were derived from both atmospherically corrected image and from rock samples in the laboratory using ASD field spectroradiometer covering spectral range of 0.4–2.5 µm. The reflectance spectra of endmembers derived from satellite image were initially compared with USGS mineral spectral library, and then after comparing with laboratory-based spectra with respect to absorption features, target minerals were identified which shows more than 70% match with the USGS and laboratory spectra. These minerals were also cross-checked with the reported litho-sequence of the area. Minerals derived from laboratory and image spectra are indicative of hydrothermally altered outer thermal aureole which is also corroborated by litho-structural association of the area.
A Quantitative and Comparative Analysis of Edge Detectors for Biomedical Image Identification Within Dynamical Noise Effect

Image processing plays a key role in many medical imaging applications, by automation and making delineation of regions of interest more simple. The paper describes image processing such as image properties, noise generators and edge detectors. The work deals with methods of edge detection in biomedical images using real data sets. The aim of this work are experiments providing information about the detector noise resistance. Another aim is own implementation of selected edge detection operators and an application on different types of data created by magnetic resonance imaging and computed tomography. Theoretical and experimental comparisons of edge detectors are presented.
Syllable-Level Long Short-Term Memory Recurrent Neural Network-based Language Model for Korean Voice Interface in Intelligent Personal Assistants

This study proposes a syllable-level long short-term memory (LSTM) recurrent neural network (RNN)-based language model for a Korean voice interface in intelligent personal assistants (IPAs). Most Korean voice interfaces in IPAs use word-level n -gram language models. Such models suffer from the following two problems: 1) the syntax information in a longer word history is limited because of the limitation of n and 2) The out-of-vocabulary (OOV) problem can occur in a word-based vocabulary. To solve the first problem, the proposed model uses an LSTM RNN-based language model because an LSTM RNN provides long-term dependency information. To solve the second problem, the proposed model is trained with a syllable-level text corpus. Korean words comprise syllables, and therefore, OOV words are not presented in a syllable-based lexicon. In experiments, the RNN-based language model and the proposed model achieved perplexity (PPL) of 68.74 and 17.81, respectively.
Classical logic with Mendler induction

Abstract We investigate (co-) induction in classical logic under the propositions-as-types paradigm, considering propositional, second-order and (co-) inductive types. Specifically, we introduce an extension of the Dual Calculus with a Mendler-style (co-) iterator and show that it is strongly normalizing. We prove this using a reducibility argument.
Human Motion Generation Based on GAN Toward Unsupervised 3D Human Pose Estimation

In this paper, we propose a method for generating joint angle sequences toward unsupervised 3D human pose estimation. Many researchers have proposed human pose estimation methods. So far, however, most methods have problems that require a large amount of images with supervised pose datasets to learn pose estimation models. Building such datasets is a time-consuming task. Thus, we aim to propose a method that can estimate 3D human poses without requiring training data with known poses. Toward this goal, we propose a GAN-based method for human motion generation and an optimization-based human pose estimation method. The proposed method consists of a generator that generates human pose sequence, a renderer that renders human images by changing 3D meshes based on the pose sequences generated, and a discriminator that discriminates between generated images and training data. Through an experiment based on simulated walking images, we confirmed that the proposed method can estimate the poses of body parts that are not occluded.