Reviewerly/exHarmony huggingface dataset:
A digital watermarking method based on NSCT transform and hybrid evolutionary algorithms with neural networks

This study aims to determine the watermark resistance to different attacks as well as the PSNR level, both of which are essential requirements of watermarking. In our research, we came up with an intelligent design based on NSCT-SVD that fulfills these requirements to a great extent and we managed to use different-sized images for watermark instead of using logos on the host images. Yet we were able to improve PSNR levels and resistance to various attacks. In this paper an NSCT-SVD-based smart watermark model is proposed. We first compare the PSO and PSO-GA algorithms for greater stability using larger SFs obtained by the PSO-GA-AI algorithm. The resulting host image is then decomposed by NSCT transform to obtain images below the low frequency range. Stationary Wavelet Transform (SWT) is performed once on these coefficients and the low frequency coefficients are fed to SVD. Afterwards, SWT transform is performed on the watermark image and the transform is once again taken from the HL coefficients and the LL frequencies are given to the SVD conversion. The rest of image process is insertion. This insertion process dramatically increases the visual transparency and PSNR value. The experiment shows that such a model is able to resist the repeated image attacks with better visibility and power. These results are compared before and after using SWT. We have used a PSO-based algorithm for better results on the False Positive rate in the embedding phase.
A Lightweight Convolutional Neural Network for Real and Apparent Age Estimation in Unconstrained Face Images

Real and apparent age estimation of human face has attracted increased attention due to its numerous real-world applications.Different intelligent application scenarios can benefit from these computer-based systems that predict the ages of people correctly.Automatic apparent age system is particularly useful in medical diagnosis, facial beauty product development, movie role casting, the effect of plastic surgery, and anti-aging treatment.Predicting the real and apparent age of people has been quite difficult for both machines and humans.More recently, Deep learning with Convolutional Neural Networks (CNNs) methods have been extensively used for these classification task.It has incomparable advantages in extracting discriminative image features from human faces.However, many of the existing CNN-based methods are designed to be deeper and larger with more complex layers that makes it challenging to deploy on mobile devices with resource-constrained features.Therefore, we design a lightweight CNN model of fewer layers to estimate the real and apparent age of individuals from unconstrained real-time face images that can be deployed on mobile devices.The experimental results, when analyzed for classification accuracy on FG-NET, MORPH-II and APPA-REAL, with large-scale face images containing both real and apparent age annotations, show that our model obtains a state-of-the-art performance in both real and apparent age classification when compared to state-of-the-art methods.The new results and model size, therefore, confirm the usefulness of the model on resource-constrained mobile devices.
High average-utility itemset mining with multiple minimum utility threshold: A generalized approach

High Average-Utility Itemset (HAUI) mining is an emerging pattern mining technique to extract meaningful patterns from a transaction dataset. In the past, several HAUI mining algorithms have been developed with efficient upper-bounds and pruning strategies. However, all these algorithms use a single value of the minimum average-utility threshold for all the itemsets, which limits their applicability to real-life datasets. In order to address this issue, several HAUI mining algorithms with multiple average-utility thresholds have been developed that process the items in ascending order of their minimum average-utility threshold. However, it makes them inapplicable on traditional HAUI mining algorithms. Moreover, the perturbation in preference of items may reduce the performance of the algorithms. This paper presents an HAUI mining algorithm named Generalized High Average-utility Itemset Miner (GHAIM) that processes the items in ascending order of their Average Utility Upper-Bound (AUUB) like the traditional HAUI mining algorithms. A new approach named suffix minimum average-utility is proposed to retain the downward closure property of AUUB and several pruning methods. Besides, a compact list structure is also proposed to mine the HAUIs in one phase. Several pruning methods have been introduced for reducing search space and improving efficiency. Extensive experiments were performed with different sparse and dense types of datasets to determine GHAIM efficiency compared to two existing algorithms. It was observed from the results that GHAIM outperforms both the current algorithms in run time, memory consumption, number of candidate itemsets, and scalability.
Deep saliency models : The quest for the loss function

Deep learning techniques are widely used to model human visual saliency, to such a point that state-of-the-art performances are now only attained by deep neural networks. However, one key part of a typical deep learning model is often neglected when it comes to modeling visual saliency: the choice of the loss function. In this work, we explore some of the most popular loss functions that are used in deep saliency models. We demonstrate that on a fixed network architecture, modifying the loss function can significantly improve (or depreciate) the results, hence emphasizing the importance of the choice of the loss function when designing a model. We also evaluate the relevance of new loss functions for saliency prediction inspired by metrics used in style-transfer tasks. Finally, we show that a linear combination of several well-chosen loss functions leads to significant improvements in performance on different datasets as well as on a different network architecture, thus demonstrating the robustness of a combined metric.
Multiple objects tracking by a highly decisive three-frame differencing-combined-background subtraction method with GMPFM-GMPHD filters and VGG16-LSTM classifier

Tracking of moving vehicles and pedestrians is the most important application in traffic surveillance videos. This study develops a highly efficient and fast multi-object tracking method using three-frame differencing-combined-background subtraction (TFDCBS)-coupled-automatic and fast histogram-entropy-based thresholding (HEBT) method together with GMPFM-GMPHD filters and VGG16-LSTM classifier. Here TFDCBS-HEBT methods identify the targeted objects with enclosed 3D bounding boxes and extracts multiple features from the raw images. Maximum number of error-free extracted multiple features (key points, multiple local convolutions, corners, and descriptors) are processed subsequently for object tracking by GMPFM-GMPHD Filters and an upgraded VGG16- LSTM classifier. The proposed method has been validated on KITTI 3D bounding box-dataset and its performance compared with three state-of-the-art tracking methods. Highest values of several performance parameters and the lowest computation time clearly demonstrate the promising feature of our new method for its application towards a fast and effective multi-target tracking of moving objects.
Behaviour and Reasoning Description Language (BRDL)

Abstract In this paper we present a basic language for describing human behaviour and reasoning and present the cognitive architecture underlying the semantics of the language. The language is illustrated through a number of examples showing its ability to model human reasoning, problem solving, deliberate behaviour and automatic behaviour. We expect that the simple notation and its intuitive semantics may address the needs of practitioners from non matematical backgrounds, in particular psychologists, linguists and other social scientists. The language usage is twofold, aiming at the formal modelling and analysis of interactive systems and the comparison and validation of alternative models of memory and cognition.
The impact of terrorism on formal and informal economy in African countries

Abstract This study examines the impact of terrorist attacks on the formal and informal economy for 47 African countries during the period 1996–2015. Four terrorism indicators are used, namely, domestic, transnational, uncertain and total terrorism. The empirical results are based on the two‐step Generalized Method of Moments in system. Three key findings are established. First, all terrorist indicators affect negatively the formal economy and positively the informal economy. This effect may be due not only to the size and maturity of the African economy, but also to the adoption of less conservative public policies. Second, an aggregate analysis identifying the impact of terrorism on various macroeconomic variables sheds light on the impact of terrorism on the overall situation of the African economy. Third, compared to domestic terrorism, transnational terrorism more significantly and negatively affects the formal economy and positively affects the informal economy. Political implications are discussed.
Mosaic

Relational database systems are purpose-built for a specific storage device class (e.g., HDD, SSD, or DRAM). They do not cope well with the multitude of storage devices that are competitive at their price `sweet spots'. To make use of different storage device classes, users have to resort to workarounds, such as storing data in different tablespaces. A lot of research has been done on heterogeneous storage frameworks for distributed big data query engines. These engines scale well for big data sets but are often CPU- or network-bound. Both approaches only maximize performance for previously purchased storage devices. We present Mosaic, a storage engine for scan-heavy workloads on RDBMS that manages devices in a tierless pool and provides device purchase recommendations for a specified workload and budget. In contrast to existing systems, Mosaic generates a performance/budget curve that is Pareto-optimal, along which the user can choose. Our approach uses device models and linear optimization to find a data placement solution that maximizes I/O throughput for the workload. Our evaluation shows that Mosaic provides a higher throughput at the same budget or a similar throughput at a lower budget than the state-of-the-art approaches of big data query engines and RDBMS.
Rapid path planning for Dubins vehicles under environmental currents

This paper presents a rapid (real time) solution to the minimum-time path planning problem for Dubins vehicles under environmental currents (wind or ocean currents). Real-time solutions are essential in time-critical situations (such as replanning under dynamically changing environments or tracking fast moving targets). Typically, Dubins problem requires to solve for six path types; however, due to the presence of currents, four of these path types require to solve the root-finding problem involving transcendental functions. Thus, the existing methods result in high computation times and their applicability for real-time applications is limited. In this regard, in order to obtain a real-time solution, this paper proposes a novel approach where only a subset of two Dubins path types (LSL and RSR) are used which have direct analytical solutions in the presence of currents. However, these two path types do not provide full reachability. We show that by extending the feasible range of circular arcs in the LSL and RSR path types from 2π to 4π: (1) full reachability of any goal pose is guaranteed, and (2) paths with lower time costs as compared to the corresponding 2π-arc paths can be produced. Theoretical properties are rigorously established, supported by several examples, and evaluated in comparison to the Dubins solutions by extensive Monte-Carlo simulations.
Multifactor authentication system based on color visual cryptography, facial recognition, and dragonfly optimization

Facial recognition as an authentication factor requires that facial images and features are tamper-free. Visual cryptography (VC) is commonly used for this purpose but leads to additional computational overhead and lower recognition accuracy. To address these issues, we propose a multifactor authentication (MFA) system based on facial recognition that uses VC to secure biometric data, and also as a second authentication factor. The shares generated by VC are used as authentication tokens. These tokens are verified by the same facial recognition algorithm used to recognize a live facial image of the user. This simple albeit novel approach leads to lower computational cost because the same algorithm can be used to verify both authentication factors. To maximize verification accuracy, the binary dragonfly optimization algorithm is used to maximize the quality of the recovered image from VC, as well as the accuracy of the facial recognition algorithm itself through feature selection. The combination of these separate ideas leads to a novel MFA system that is efficient and highly accurate. Experimental verification based on various face image databases depicts the proposed system’s security, efficiency, and near-ideal recognition accuracy of up to 99.81%.
Virtual sample-based deep metric learning using discriminant analysis

Deep metric learning (DML) has been designed to maximize the inter-class variance that is the distance between embedding features belonging to different classes. Since conventional DML techniques do not consider the statistical characteristics of the embedding space, or they calculate similarity using only a given feature, they make it difficult to adaptively reflect the characteristics of the feature distribution during the learning process. This paper proposes a virtual metric loss (VML) incorporating with embedding features by using virtual samples produced through linear discriminant analysis (LDA). This study is valuable in that it proposes a new metric that can learn inter-class variance of embedding features by integrating discriminant analysis and metric learning which have a common purpose of inter-class variance analysis. In addition, we theoretically analyze the eigenvalue equation problem and the degree of stabilization in the embedding space. We have verified the performance of the proposed VML through extensive experiments on large and few-shot retrieval datasets. For example, in the CUB200-2011 dataset, the VML showed a recall rate about 0.7% higher than a state-of-the-art method. We also explored a new similarity through virtual samples and adjusted the difficulty of embedding features, thereby confirming the possibility of expanding virtual samples into various fields of pattern recognition.
Exploiting domain knowledge to address multi-class imbalance and a heterogeneous feature space in classification tasks for manufacturing data

Classification techniques are increasingly adopted for quality control in manufacturing, e.g., to help domain experts identify the cause of quality issues of defective products. However, real-world data often imply a set of analytical challenges, which lead to a reduced classification performance. Major challenges are a high degree of multi-class imbalance within data and a heterogeneous feature space that arises from the variety of underlying products. This paper considers such a challenging use case in the area of End-of-Line testing, i.e., the final functional test of complex products. Existing solutions to classification or data pre-processing only address individual analytical challenges in isolation. We propose a novel classification system that explicitly addresses both challenges of multi-class imbalance and a heterogeneous feature space together. As main contribution, this system exploits domain knowledge to systematically prepare the training data. Based on an experimental evaluation on real-world data, we show that our classification system outperforms any other classification technique in terms of accuracy. Furthermore, we can reduce the amount of rework required to solve a quality issue of a product.
Design of Interval Type-2 Information Granules Based on the Principle of Justifiable Granularity

Information granules are concise abstract descriptors of data supported by experimental evidence. They summarize the data by forming a small collection of well justified information granule. Fuzzy sets of type-2 generalize type-1 fuzzy sets. In this article, we present an original design of interval type-2 information granules based on a collection of type-1 fuzzy sets by engaging the principle of justifiable granularity. This principle generates an information granule by maximizing a product of two generic characteristics of the granule, such as coverage and specificity. Given a collection of type-1 fuzzy sets, the result of the principle comes in a form of a single type-2 information granule. In general, we emphasize the effect of type elevation of information granules by stressing that a family of type- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> information granules gives rise to a single type-( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> +1) information granule. The overall optimization process is discussed along with a series of related optimization procedures. A series of experimental studies is included to illustrate the essence of the approach.
Multi-Source Medical Data Integration and Mining for Healthcare Services

With the advent of Internet of Health (IoH) age, traditional medical or healthy services are gradually migrating to the Web or Internet and have been producing a considerable amount of medical data associated with patients, doctors, medicine, medical infrastructure and so on. Effective fusion and analyses of these IoH data are of positive significances for the scientific disaster diagnosis and medical care services. However, IoH data are often distributed across different departments and contain partial user privacy. Therefore, it is often a challenging task to effectively integrate or mine the sensitive IoH data, during which user privacy is not disclosed. To overcome the above difficulty, we put forward a novel multi-source medical data integration and mining solution for better healthcare services, named PDFM (Privacy-free Data Fusion and Mining). Through PDFM, we can search for similar medical records in a time-efficient and privacy-preserving manner, so as to offer patients with better medical and health services. A group of experiments are enacted and implemented to demonstrate the feasibility of the proposal in this work.
An indexing scheme and descriptor for 3D object retrieval based on local shape querying

A binary descriptor indexing scheme based on Hamming distance called the Hamming tree for local shape queries is presented. A new binary clutter resistant descriptor named Quick Intersection Count Change Image (QUICCI) is also introduced. This local shape descriptor is extremely small and fast to compare. Additionally, a novel distance function called Weighted Hamming applicable to QUICCI images is proposed for retrieval applications. The effectiveness of the indexing scheme and QUICCI is demonstrated on 828 million QUICCI images derived from the SHREC2017 dataset, while the clutter resistance of QUICCI is shown using the clutterbox experiment.
On pairing Huber support vector regression

In this paper, a novel and efficient pairing support vector regression learning method using ε−insensitive Huber loss function (PHSVR) is proposed where the ε−insensitive zone having flexible shape is determined by tightly fitting the training samples. Our approach leads to solving a pair of unconstrained minimization problems in primal and the solutions are obtained by two algorithms: a functional iterative (FPHSVR) and Newton iterative (NPHSVR) algorithms. The finite termination of the Newton method to its global minimum solution is proved. The significant advantages of the proposed method are the robustness, generalization ability and learning speed. Experiments performed on a series of synthetic data sets, polluted by different types of noise including heteroscedastic noise and outliers, and on real-world benchmark data sets confirm the effectiveness and superiority of the proposed method.
Real-time Unmanned Aerial Vehicle Cruise Route Optimization for Road Segment Surveillance using Decomposition Algorithm

SUMMARY Unmanned aerial vehicle (UAV) was introduced for nondeterministic traffic monitoring, and a real-time UAV cruise route planning approach was proposed for road segment surveillance. First, critical road segments are defined so as to identify the visiting and unvisited road segments. Then, a UAV cruise route optimization model is established. Next, a decomposition-based multi-objective evolutionary algorithm (DMEA) is proposed. Furthermore, a case study with two scenarios and algorithm sensitivity analysis are conducted. The analysis result shows that DMEA outperforms other two commonly used algorithms in terms of calculation time and solution quality. Finally, conclusions and recommendations on UAV-based traffic monitoring are presented.
Exploring How Role and Background Influence Through Analysis of Spatial Dialogue in Collaborative Problem-Solving Games

This study examines how different roles and background knowledge transform players' dyadic conversations into spatial dialogues in a virtual cellular biology game. Cellverse is a collaborative virtual reality (VR) game designed to teach cell biology. Players work in pairs, assuming the role of either a Navigator, with reference material and a global view through a tablet, or an Explorer, with a more detailed interactive view of the cell through a VR headset and hand controllers. The game is designed so players must collaborate in order to complete the game. Our results show that roles influenced their reference perspectives at a level of statistical significance. Furthermore, players with high prior knowledge tried to reduce their partner's mental effort by giving spatial information from their point of view, thus producing fewer occurrences of spatial unawareness. Results of this study suggest that designers can build in different roles and leverage different background knowledge to prompt effective partnerships during collaborative games.
Enhancing Interaction and Accessibility in Museums and Exhibitions with Augmented Reality and Screen Readers

Throughout the evolution of humanity, technologies have served as support for new evolutionary horizons. It is an unambiguous fact that technologies have positively influenced the masses, but they have also brought a remoteness with local cultures, often making them oblivious. Among the new technologies and forms of interaction, we have augmented reality and screen readers that allow the device to read the content. This paper presents AIMuseum. It aims to facilitate accessing and interacting with cultural environments for people with different abilities, combining the use of technologies with local museums, artworks, and exhibitions. The work was evaluated with 38 users, ranging from 16 to 41 years old, and five declared having one type of disability. They used the application and answered a questionnaire. The results showed a positive experience and improved the users' interest in the artworks and their additional information.