Reviewerly/exHarmony huggingface dataset:
Density peaks clustering based on geodetic distance and dynamic neighbourhood

Density peaks clustering algorithm uses Euclidean distance as a measure of similarity between the samples and it can achieve a good clustering effect when processing the manifold datasets. Utilisin...
Secret Communication Systems Using Chaotic Wave Equations with Neural Network Boundary Conditions

In a secret communication system using chaotic synchronization, the communication information is embedded in a signal that behaves as chaos and is sent to the receiver to retrieve the information. In a previous study, a chaotic synchronous system was developed by integrating the wave equation with the van der Pol boundary condition, of which the number of the parameters are only three, which is not enough for security. In this study, we replace the nonlinear boundary condition with an artificial neural network, thereby making the transmitted information difficult to leak. The neural network is divided into two parts; the first half is used as the left boundary condition of the wave equation and the second half is used as that on the right boundary, thus replacing the original nonlinear boundary condition. We also show the results for both monochrome and color images and evaluate the security performance. In particular, it is shown that the encrypted images are almost identical regardless of the input images. The learning performance of the neural network is also investigated. The calculated Lyapunov exponent shows that the learned neural network causes some chaotic vibration effect. The information in the original image is completely invisible when viewed through the image obtained after being concealed by the proposed system. Some security tests are also performed. The proposed method is designed in such a way that the transmitted images are encrypted into almost identical images of waves, thereby preventing the retrieval of information from the original image. The numerical results show that the encrypted images are certainly almost identical, which supports the security of the proposed method. Some security tests are also performed. The proposed method is designed in such a way that the transmitted images are encrypted into almost identical images of waves, thereby preventing the retrieval of information from the original image. The numerical results show that the encrypted images are certainly almost identical, which supports the security of the proposed method.
Learning Product Rankings Robust to Fake Users

In many online platforms, customers' decisions are substantially influenced by product rankings as most customers only examine a few top-ranked products. Concurrently, such platforms also use the same data corresponding to customers' actions to learn how these products must be ranked or ordered. These interactions in the underlying learning process, however, may incentivize sellers to artificially inflate their position by employing fake users, as exemplified by the emergence of click farms. Motivated by such fraudulent behavior, we study the ranking problem of a platform that faces a mixture of real and fake users who are indistinguishable from one another. We first show that existing learning algorithms---that are optimal in the absence of fake users---may converge to highly sub-optimal rankings under manipulation by fake users. To overcome this deficiency, we develop efficient learning algorithms under two informational environments: in the first setting, the platform is aware of the number of fake users, and in the second setting, it is agnostic to the number of fake users. For both these environments, we prove that our algorithms converge to the optimal ranking, while being robust to the aforementioned fraudulent behavior; we also present worst-case performance guarantees for our methods, and show that they significantly outperform existing algorithms. At a high level, our work employs several novel approaches to guarantee robustness such as: (i) constructing product-ordering graphs that encode the pairwise relationships between products inferred from the customers' actions; and (ii) implementing multiple levels of learning with a judicious amount of bi-directional cross-learning between levels. Overall, our results indicate that online platforms can effectively combat fraudulent users without incurring large costs by designing new learning algorithms that guarantee efficient convergence even when the platform is completely oblivious to the number and identity of the fake users.
Single-shot 4-step phase-shifting multispectral fringe projection profilometry

Phase-shifting profilometry (PSP) is considered to be the most accurate technique for phase retrieval with fringe projection profilometry (FPP) systems. However, PSP requires that multiple phase-shifted fringe patterns be acquired, usually sequentially, which has limited PSP to static or quasi-static imaging. In this paper, we introduce multispectral 4-step phase-shifting FPP that provides 3D imaging using a single acquisition. The method enables real-time profilometry applications. A single frame provides all four phase-shifted fringe patterns needed for the PSP phase retrieval algorithm. The multispectral nature of the system ensures that light does not leak between the spectral bands, which is a common problem in simultaneous phase-shifting with color cameras. With the use of this new concept, custom composite patterns containing multiple patterns can be acquired with a single acquisition.
Delineation of the porphyry-skarn mineralized zones (NW Turkey) using concentration–volume fractal model

This study is carried out for delineation of the Tepeoba porphyry-skarn Cu-Mo ± Au mineralized zones at the Biga peninsula (NW Turkey) using the concentration–volume (C–V) fractal model. The power-law C–V relationships of Cu, Mo, and Au reveal five mineralized zones of Cu, three zones of Mo, and five zones for gold in the Tepeoba deposit. The main phase of the mineralization has average ore grades of 0.257% Cu, 0.357% Mo and 5.3083 ppm Au. Cu–Mo sulfide-rich hypogene ore zone overlain by mineralized oxidation zone are encompassed by three main alteration types, which are represented by biotite ± muscovite-K-feldspar, actinolite-albite and outer chlorite-epidote-calcite mineral associations occurring within the porphyritic microgranite and hornfels in the mine area. The delineated mineralization trend, based on the C–V fractal model, suggested that Cu and Mo enrichment zones were controlled by the same geochemical processes in the deposit due to their similar trends with the C–V log-log plot. Cu and Mo occurred mainly within the breccia zones along with stockwork veining at the contact between the hornfels and the biotite (±muscovite)-K-feldspar-altered Eybek microgranite. The main mineralization zone of Au developed in the oxidation zone due to of supergene enrichment processes.
Extraction and segmentation method of laser stripe in linear structured light scanner

Line structured light scanning technology is extensively applied in three-dimensional (3D) reconstruction due to its advantages of having high precision and being non-contact. To quickly and accurately obtain the feature information of the light stripe center, a method of extraction and adaptive segmentation of the light stripe center is proposed for 3D measurement of structured light. First, a subpixel extraction method is presented to extract the light strip center; it quickly determines the normal direction of the light stripe and accurately extracts the light stripe center in the normal direction by employing the improved gray gravity method. Second, an adaptive contour segmentation method based on slope difference and dynamic neighborhood optimization is presented. Finally, the proposed method is adopted to measure the geometric size of the complex manufactured parts. The experimental results demonstrate that the maximum measurement error is 0.15 mm and the accuracy is <1 % , indicating that the method is accurate enough to be applied to various kinds of measurements.
Probing Multilingual Language Models for Discourse

Pre-trained multilingual language models have become an important building block in multilingual natural language processing.In the present paper, we investigate a range of such models to find out how well they transfer discourse-level knowledge across languages.This is done with a systematic evaluation on a broader set of discourse-level tasks than has been previously been assembled.We find that the XLM-RoBERTa family of models consistently show the best performance, by simultaneously being good monolingual models and degrading relatively little in a zero-shot setting.Our results also indicate that model distillation may hurt the ability of cross-lingual transfer of sentence representations, while language dissimilarity at most has a modest effect.We hope that our test suite, covering 5 tasks with a total of 22 languages in 10 distinct families, will serve as a useful evaluation platform for multilingual performance at and beyond the sentence level.
Classification of White Blood Cells Using CNN and Con-SVM

White blood cells are one of the most important cells for the human body that make up the human immune system. These cells in the blood can contain important information for the diagnosis of many diseases as well as protecting the body. Nowadays, blood analysis performed by doctors can cause both waste of time and human errors. A support diagnosis system can be created to prevent these errors, to perform rapid diagnosis and to reduce the workload. In this study, 12442 white blood cell images consisting of four types of white blood cells were tried to be classified. These images were first classified with CNN (Convolutional Neural Networks), then classification is performed using Con-SVM (Convolutional features - Support Vector Machines). While an accuracy rate of 83.91% was obtained with the CNN model, an accuracy rate of 85.96% was observed with the Con-SVM model.
Genetic Programming with Archive for Dynamic Flexible Job Shop Scheduling

Genetic programming (GP) has achieved great success in evolving effective scheduling rules to make real-time decisions in dynamic flexible job shop scheduling (DFJSS). To improve generalization, a commonly used strategy is to change the training simulation(s) at each generation of the GP process. However, with such a simulation rotation, GP may lose potentially promising individuals that happen to perform poorly in one particular generation. To address this issue, this paper proposed a new multi-tree GP with archive (MTAGP) to evolve the routing and sequencing rules for DFJSS. The archive is used to store the potentially promising individuals of each generation during evolution of genetic programming. The individuals in the archive can then be fully utilized when the simulation is changed in subsequent generations. Through extensive experimental tests, the MTAGP algorithm proposed in this paper is more effective than the multi-tree GP without archive algorithm in a few scenarios. Further experiments were carried out to analyze the use of the archive and some possible guesses were ruled out. We argue that the use of archives does increase the diversity of the population. However, the number of individuals in the archive that ranked in the top five of the new population is small. Therefore, the archive may not be able to greatly improve the performance. In the future, we will investigate better ways to use the archive and better ways to update individuals in the archive.
A Flexible Variable-length Particle Swarm Optimization Approach to Convolutional Neural Network Architecture Design

The great success of convolutional neural networks (CNNs) in image classification benefits from the powerful feature learning abilities of their architectures. However, arbitrarily constructing these architectures is very costly in terms of manpower and computation resources. Recently, particle swarm optimization (PSO), as a promising evolutionary computation (EC) method, has been used to automatically search for the CNN architectures and achieved encouraging results on image classification, but these existing methods are not well-designed and/or do not have good search capabilities. In this paper, a flexible variable-length PSO algorithm is proposed for automated CNN architecture design for image classification tasks. Particularly, an improved encoding scheme is proposed to truly break the fixed-length representation constraint of PSO and encode the parameters in a more meaningful way. In addition, a novel velocity and position updating approach is developed to update variable-length particles. Experiments on four benchmark datasets are carried out to confirm the superiority of the proposed algorithm. It is shown that the proposed method leads to better results comparing to the state-of-the-art algorithms in terms of classification performance, parameter size and computational complexity.
Blind Modulation Identification Algorithm For Two-Path Successive Relaying Systems

Modulation identification, a major task of intelligent receivers, is of vital importance for applications in military and civilian fields. In this work, we develop and analyze a blind modulation identification algorithm for two-path successive relaying systems. Analytical expressions for the correlation functions adopted as the basis of the proposed algorithm are derived, and the computational cost of the algorithm is addressed. By taking advantage of the space-time redundancy, we mathematically prove that a group of modulation types exhibits peaks for specific correlation functions while the other group does not. Exploiting this feature, we propose a binary tree hypothesis test for decision-making. A packet length estimator is also introduced as an auxiliary task for the proposed algorithm. The proposed algorithm does not require information about channel coefficients and noise power. The identification performance of the proposed algorithm is evaluated through Monte Carlo simulations under different conditions. Results show that the performance of the proposed algorithm is remarkably better than the traditional algorithms.
Masked Contrastive Learning for Anomaly Detection

Detecting anomalies is one fundamental aspect of a safety-critical software system, however, it remains a long-standing problem. Numerous branches of works have been proposed to alleviate the complication and have shown promising results. In particular, self-supervised learning based methods are spurring interest due to their capability of learning diverse representations without additional labels. Among self-supervised learning tactics, contrastive learning is one specific framework showing pronounced results in various fields including anomaly detection. However, the primary objective of contrastive learning is to learn task-agnostic features without any labels, which is not entirely suited to discern anomalies. In this paper, we propose a task-specific variant of contrastive learning named masked contrastive learning, which is more befitted for anomaly detection. Moreover, we propose a new inference method dubbed self-ensemble inference that further boosts performance by leveraging the ability learned through auxiliary self-supervision tasks. By combining our models, we can outperform previous state-of-the-art methods by a significant margin on various benchmark datasets.
Complex Environment Path Planning for Unmanned Aerial Vehicles

Flying safely in complex urban environments is a challenge for unmanned aerial vehicles because path planning in urban environments with many narrow passages and few dynamic flight obstacles is difficult. The path planning problem is decomposed into global path planning and local path adjustment in this paper. First, a branch-selected rapidly-exploring random tree (BS-RRT) algorithm is proposed to solve the global path planning problem in environments with narrow passages. A cyclic pruning algorithm is proposed to shorten the length of the planned path. Second, the GM(1,1) model is improved with optimized background value named RMGM(1,1) to predict the flight path of dynamic obstacles. Herein, the local path adjustment is made by analyzing the prediction results. BS-RRT demonstrated a faster convergence speed and higher stability in narrow passage environments when compared with RRT, RRT-Connect, P-RRT, 1-0 Bg-RRT, and RRT*. In addition, the path planned by BS-RRT through the use of the cyclic pruning algorithm was the shortest. The prediction error of RMGM(1,1) was compared with those of ECGM(1,1), PCGM(1,1), GM(1,1), MGM(1,1), and GDF. The trajectory predicted by RMGM(1,1) was closer to the actual trajectory. Finally, we use the two methods to realize path planning in urban environments.
Pre-trained CNNs Models for Content based Image Retrieval

Content based image retrieval (CBIR) systems is a common recent method for image retrieval and is based mainly on two pillars extracted features and similarity measures.Low level image presentations, based on colour, texture and shape properties are the most common feature extraction methods used by traditional CBIR systems.Since these traditional handcrafted features require good prior domain knowledge, inaccurate features used for this type of CBIR systems may widen the semantic gap and could lead to very poor performance retrieval results.Hence, features extraction methods, which are independent of domain knowledge and have automatic learning capabilities from input image are highly useful.Recently, pre-trained deep convolution neural networks (CNN) with transfer learning facilities have ability to generate and extract accurate and expressive features from image data.Unlike other types of deep CNN models which require huge amount of data and massive processing time for training purposes, the pre-trained CNN models have already trained for thousands of classes of large-scale data, including huge images and their information could be easily used and transferred.ResNet18 and SqueezeNet are successful and effective examples of pre-trained CNN models used recently in many machine learning applications, such as classification, clustering and object recognition.In this study, we have developed CBIR systems based on features extracted using ResNet18 and SqueezeNet pre-trained CNN models.Here, we have utilized these pre-trained CNN models to extract two groups of features that are stored separately and then later are used for online image searching and retrieval.Experimental results on two popular image datasets Core-1K and GHIM-10K show that ResNet18 features based on the CBIR method have overall accuracy of 95.5% and 93.9% for the two datasets, respectively, which greatly outperformed the traditional handcraft features based on the CBIR method.
An Examination of Fairness of AI Models for Deepfake Detection

Recent studies have demonstrated that deep learning models can discriminate based on protected classes like race and gender. In this work, we evaluate bias present in deepfake datasets and detection models across protected subgroups. Using facial datasets balanced by race and gender, we examine three popular deepfake detectors and find large disparities in predictive performances across races, with up to 10.7% difference in error rate between subgroups. A closer look reveals that the widely used FaceForensics++ dataset is overwhelmingly composed of Caucasian subjects, with the majority being female Caucasians. Our investigation of the racial distribution of deepfakes reveals that the methods used to create deepfakes as positive training signals tend to produce ``irregular" faces - when a person’s face is swapped onto another person of a different race or gender. This causes detectors to learn spurious correlations between the foreground faces and fakeness. Moreover, when detectors are trained with the Blended Image (BI) dataset from Face X-Rays, we find that those detectors develop systematic discrimination towards certain racial subgroups, primarily female Asians.
Determinants of Financial Inclusion: New Evidence from Panel Data Analysis

We find evidence that favorable socioeconomic conditions in terms of income, financial development, institutional quality, population growth rate, and human development foster the development of inclusive finance. There are differences in the determinants of financial inclusion as well as its three dimensions between country groups.
A knowledge-based model for context-aware smart service systems

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Silhouette-Based Synthetic Data Generation For 3D Human Pose Estimation With A Single Wrist-Mounted 360° Camera

In this paper, we propose a framework for 3D human pose estimation with a single 360° camera mounted on the user’s wrist. Perceiving a 3D human pose with such a simple setting has remarkable potential for various applications (e.g., daily-living activity monitoring, motion analysis for sports enhancement). However, no existing work has tackled this task due to the difficulty of estimating a human pose from a single camera image in which only a part of the human body is captured and the lack of training data. Therefore, we propose an effective method for translating wrist-mounted 360° camera images into 3D human poses. We also propose silhouette-based synthetic data generation dedicated to this task, which enables us to bridge the domain gap between real-world data and synthetic data. We achieved higher estimation accuracy quantitatively and qualitatively compared with other baseline methods.
Crypto-Watermarking Algorithm Using Weber’s Law and AES: A View to Transfer Safe Medical Image

We propose a novel method for medical image watermarking in the DCT domain using the AES encryption algorithm. First, we decompose the original medical image into subblocks of 8 × 8. Besides, we apply the DCT and the quantization, respectively, to each subblock. However, in the DCT domain, an adequate choice of the DCT coefficients according to the quantization table in the middle frequencies band is performed. After that, we embed the patient’s data into the corresponding medical image. The insertion step is carried out just after the quantization phase. To increase the robustness, we encrypt the watermarked medical images by using the AES algorithm based on chaotic technique. Arnold’s cat map is used to shuffle the pixel values, and a chaotic Henon map is utilized to generate an aleatory sequence for the AES algorithm. The shuffled watermarked image is encrypted using the modified AES algorithm. The constant of Weber is used to choose the suitable visibility factor for embedding a watermark with high robustness. To control identification, after application of attacks, we use the serial turbo code for correction of the watermark to recover the data inserted. The average peak signal-to-noise ratio (PSNR) of the medical images obtained is 61,7769 dB. Experimental results demonstrate the robustness of the proposed schema against various types of attacks.