Reviewerly/exHarmony huggingface dataset:
Security of Public-Key Cryptosystems Based on Chebyshev Polynomials Over $\mathbb Z/p^{k}\mathbb Z$

A public-key cryptosystem using Chebyshev polynomials defined on a finite set has recently been developed, which is a kind of chaos-based cryptography. The security of this cryptosystem relies on the difficulty of finding the degree of Chebyshev polynomials from given parameters. In this brief, we propose polynomial time algorithms to identify the degree of Chebyshev polynomials modulo a prime power. We demonstrate that the cryptosystem based on Chebyshev polynomials modulo a prime power is not secure. This result also means that there are no commutative polynomials for constructing public-key cryptosystems modulo a prime power.
CMed: Crowd Analytics for Medical Imaging Data

We present a visual analytics framework, CMed, for exploring medical image data annotations acquired from crowdsourcing. CMed can be used to visualize, classify, and filter crowdsourced clinical data based on a number of different metrics such as detection rate, logged events, and clustering of the annotations. CMed provides several interactive linked visualization components to analyze the crowd annotation results for a particular video and the associated workers. Additionally, all results of an individual worker can be inspected using multiple linked views in our CMed framework. We allow a crowdsourcing application analyst to observe patterns and gather insights into the crowdsourced medical data, helping him/her design future crowdsourcing applications for optimal output from the workers. We demonstrate the efficacy of our framework with two medical crowdsourcing studies: polyp detection in virtual colonoscopy videos and lung nodule detection in CT thin-slab maximum intensity projection videos. We also provide experts' feedback to show the effectiveness of our framework. Lastly, we share the lessons we learned from our framework with suggestions for integrating our framework into a clinical workflow.
Considerations in the application of machine learning to aqueous geochemistry: Origin of produced waters in the northern U.S. Gulf Coast Basin

Since the advent of modern computing, geochemists have increasingly relied on computers to garner efficiencies in calculations, data analysis, and data presentation. Entirely new fields, such as Monte Carlo-based simulation and geochemical modeling, have developed under this paradigm. With continued growth in computing power, machine learning has become an increasingly popular tool in aqueous geochemistry. However, continued reliance on algorithms to perform mathematical calculations can lead to paths of not understanding how to properly prepare information for models or not the reasons behind apparent patterns in the output. Machine learning algorithms can be heavily impacted by what variables are chosen for the model and how data are pre-processed, including handling of missing and censored values (e.g., above or below a detection limit). We propose an approach of parsimonious variable selection, based partially on the signal-to-noise ratio, and suggest and discuss strategies for handling missing and censored data. An example of unsupervised machine learning, using emergent self-organizing map analysis, is applied to water from oil and gas wells in the northern U.S. Gulf Coast Basin, whose composition is controlled by different processes and is derived from various origins. Findings from this investigation suggest five groups of water samples are present, two of which were not identified using conventional data analysis methods. One notable result is that brines derived from seawater evaporation, presumably waters from which the Jurassic Louann salt precipitated, have migrated upward into shallower reservoirs across the study area. This work demonstrates that focus on understanding data quality and exercises to better interpret the output from numerical models continue to be critical skills to further take advantage of applying machine learning to geochemistry.
Near Infrared Spectroscopic Study of Trioctahedral Chlorites and Its Remote Sensing Application

Abstract The mineral chemistry of thirteen trioctahedral chlorite samples from four regions in northwestern China, having a wide range of Fe and Mg contents and relatively constant Al and Si contents, were studied via raman spectroscopy, near infrared (NIR) spectroscopy, X-ray diffraction (XRD) analysis and electron - probe microanalysis. Five absorption features of the twenty samples near 4525, 4440, 4350, 4270 and 4180 cm −1 were observed, and two diagnostic features at 4440 and 4270 cm −1 were recognized. Assignments of the two diagnostic features were made for two combination bands (( ν + δ ) ( AlAl ) O − OH and ( ν + δ ) ( SiAl ) O − OH ) by regression with Raman fundamental absorptions. Furthermore, the determinant factors of the NIR band position were found by comparing the band positions with relative components. The results showed that Fe 2+ /(Fe 2+ +Mg) values are negatively correlated with the two NIR combination bands. The findings provide an interpretation of the NIR band formation and demonstrate a simple way to use NIR spectroscopy to discriminate between chlorites with different components. More importantly, a simple example of mapping Fe-rich and Mg-rich chlorites were executed using remote sensing data based on this theory. The spectroscopic detection of mineral chemical variations in chlorites provides geologists with a tool with which to collect information on hydrothermal alteration zones from hyperspectral-resolution remote sensing data.
Multicontext multitask learning networks for mass detection in mammogram

Purpose In this paper, for the purpose of accurate and efficient mass detection, we propose a new deep learning framework, including two major stages: Suspicious region localization (SRL) and Multicontext Multitask Learning (MCMTL) . Methods In the first stage, SRL focuses on finding suspicious regions [regions of interest (ROIs)] and extracting multisize patches of these suspicious regions. A set of bounding boxes with different size is used to extract multisize patches, which aim to capture diverse context information. In the second stage, MCMTL networks integrate features from multisize patches of suspicious regions for classification and segmentation simultaneously, where the purpose of this stage is to keep the true positive suspicious regions and to reduce the false positive suspicious regions. Results According to the experimental results on two public datasets (i.e., CBIS‐DDSM and INBreast), our method achieves the overall performance of 0.812 TPR@2.53 FPI and 0.919 TPR@0.12 FPI on test sets, respectively. Conclusions Our proposed method suggests comparable performance to the state‐of‐the‐art methods.
Ontology-Based Image Classification and Annotation

With the rapid growth of image collections, image classification and annotation has been active areas of research with notable recent progress. Bag-of-Visual-Words (BoVW) model, which relies on building visual vocabulary, has been widely used in this area. Recently, attention has been shifted to the use of advanced architectures which are characterized by multi-level processing. Hierarchical Max-Pooling (HMAX) model has attracted a great deal of attention in image classification. To improve image classification and annotation, several approaches based on ontologies have been proposed. However, image classification and annotation remain a challenging problem due to many related issues like the problem of ambiguity between classes. This problem can affect the quality of both classification and annotation results. In this paper, we propose an ontology-based image classification and annotation approach. Our contributions consist of the following: (1) exploiting ontological relationships between classes during both image classification and annotation processes; (2) combining the outputs of hypernym–hyponym classifiers to lead to a better discrimination between classes; and (3) annotating images by combining hypernym and hyponym classification results in order to improve image annotation and to reduce the ambiguous and inconsistent annotations. The aim is to improve image classification and annotation by using ontologies. Several strategies have been experimented, and the obtained results have shown that our proposal improves image classification and annotation.
A Study on Multi-objective Chaotic Evolution Algorithms Using Multiple Chaotic Systems

We investigate the optimization performance of multi-objective chaotic evolution (MOCE) algorithm with implementations using different chaotic systems. A comparison experiment of MOCE algorithms with four chaotic systems are employed in MOCE to analyse whether chaotic systems will affect the optimization performance of the MOCE algorithms. We analyze and discuss the performance of the MOCE algorithms implemented using different chaotic systems. Four chaotic systems are introduced in this work, i.e., the logistic map, the Hénon map, the tent map, and the Gauss map, respectively. The number of Pareto solution and the diversity of Pareto solution are two evaluation metrics to evaluate the performance of the multi-objective optimization algorithm. We apply the statistical tests to analyse and investigate the number of Pareto solution and their diversity. The evaluation results indicate that the MOCE with the logistic map has the best optimization performance in both the number of Pareto solution and their diversity. The statistical significance demonstrates that chaotic systems have a great influence on the optimization performance of MOCE algorithms.
Fusion Convolutional Attention Network for Opinion Spam Detection

Opinion spam detection is a critical task in opinion mining. Current researches mainly focus on manually designing the text or discrete user features, or concatenating the review text and user features as the representation of the review. However, these methods ignore the impact of user’s preferences on review texts. Because of their different purposes, spammers and non-spammers usually show different preferences. These user-level differences are hard to be captured from the single review level by previous methods. In this paper, we propose a novel Fusion Convolutional Attention Network (FCAN) to embed the user-level information into a continuous vector space, the representations of which capture essential clues such as user profiles or preferences. Such user representation, in turn, facilitates learning better user-aware textual representation at word and sentence level. Experimental results on four real-world datasets from different platforms demonstrate that our method significantly outperforms the state-of-the-art methods and can be easily expanded to different datasets and platforms.
A Performance Comparison of Loss Functions

Generally, the deep neural network learns by way of a loss function, which is an approach to evaluate how well given dataset is predicted on a particular network architecture (or network model). If the prediction deviates too far from real data, a loss function would generate a very large value. Progressively, with the help of some optimization function, the loss function lowers the prediction error by providing the network architecture with information that can control the weights of the network architecture. Thus, the loss functions plays an important role in training the network architecture.Recently, several researchers have studied various loss functions such as Softmax, Modified softmax, Angular softmax, Additive-Margin softmax, Arcface, Center, and Focal losses. In this manuscript, we propose a new and simple loss function that just adds the existing loss functions. In addition, we conduct experiments with the MNIST dataset in order to compare the performance between all loss functions including the proposed and the existing loss functions. Resultingly, the experiments show that the proposed loss function is visibly superior to the ability to classify digit images. The experimental results also indicate that Arcface loss and Additive-Margin loss functions satisfy predefined test accuracy most quickly under two and three dimensional embedding, respectively. The fast learning ability of the both loss functions has the advantage of providing relatively high accuracy even when the number of train data is small.
Progressive Bi-C3D Pose Grammar for Human Pose Estimation

In this paper, we propose a progressive pose grammar network learned with Bi-C3D (Bidirectional Convolutional 3D) for human pose estimation. Exploiting the dependencies among the human body parts proves effective in solving the problems such as complex articulation, occlusion and so on. Therefore, we propose two articulated grammars learned with Bi-C3D to build the relationships of the human joints and exploit the contextual information of human body structure. Firstly, a local multi-scale Bi-C3D kinematics grammar is proposed to promote the message passing process among the locally related joints. The multi-scale kinematics grammar excavates different levels human context learned by the network. Moreover, a global sequential grammar is put forward to capture the long-range dependencies among the human body joints. The whole procedure can be regarded as a local-global progressive refinement process. Without bells and whistles, our method achieves competitive performance on both MPII and LSP benchmarks compared with previous methods, which confirms the feasibility and effectiveness of C3D in information interactions.
Cybersecurity and the Blockchain: Preventing the Insertion of Child Pornography Images

The fight against child pornography on the Internet is difficult due to the inability of controlling the dissemination of content. While the cryptocurrency applications of a blockchain, such as Bitcoin, primarily store financial data in blockchain transactions, numerous approaches to arbitrarily insert other types of data are possible, including content in the form of readable text and images. Thus, considering the blockchain principles of decentralization, immutability, censorship resistance, and net neutrality, we are suddenly presented with a set of new challenges for mitigating the storage and distribution of illicit and potentially illegal data. Such a novel cybersecurity threat requires an adequate solution within a decentralized paradigm. In stark contrast to the countermeasures proposed in literature for filtering out illicit content from other legitimate data, this study proposes the first appropriately designed solution for effectively mitigating these threats whilst maintaining fidelity to the underlying core principles of the blockchain. Through the designed approach, arbitrary images in blockchain transactions are detected, and whilst leveraging an off-chain content filtering service, successful discrimination is achieved between clean and illicit embedded data. The proposed methodology was evaluated directly on the Bitcoin blockchain, successfully demonstrating a pioneering and viable approach for thwarting the insertion of child pornography images. The presented solution within this research is made available on GitHub [1] for open-source use.
The Effect of International Financial Reporting Standards on the Association between Foreign Direct Investment and Economic Growth: Evidence from Selected Countries in Africa

The study aims at examining how IFRS adoption influences the relationship existing among foreign direct investment (FDI) and economic growth. The data consist of 12 developing economies that are the highest recipients of FDI in Africa, and the years of study are from 1996-2018. Using Ordinary Least Square and Generalized Least Square method of estimation, the result shows that IFRS is significantly positive to FDI. With FDI inflows in these countries, the result provides evidence that Non fully-IFRS adopted countries experience higher inflows of FDI than the fully-IFRS adopted countries. We find FDI inflows to also have positive influence on economic growth. The interaction of FDI and IFRS also influences economic growth positively when further analysis was carried on. The results provide evidence of positive relation among FDI, IFRS, and economic growth. IFRS adoption promotes FDI inflows which consequently spurs economic growth. There is, therefore, the need for policymakers to ensure adoption and enforcement of IFRS.
A Novel Entropy-based Reversible Data Hiding during Encryption

Recent advances in telemedicine services involve frequent communication and analysis of medical images. Confidentiality of medical images is a major concern in medical image transmission which can be achieved through image encryption. In this paper, we propose a new entropy-based reversible data hiding scheme through encryption for secure and confidential medical image transmission. The scheme discussed in this paper allows the embedding of electronic patient record (EPR) or diagnosis results in the image section in a reversible manner. In the proposed scheme, the encryption process is modified in such a way that the sender can embed secret message bits into the image during encryption. Further, at the receiver side, the embedded information can be extracted along with the recovery of the original image through an entropy-based image analysis during image decryption phase. The experimental study on standard images shows that the proposed scheme is able to achieve a good embedding rate with lesser bit error rate.
Joint Sketch-Attribute Learning for Fine-Grained Face Synthesis

The photorealism of synthetic face images has been significantly improved by generative adversarial networks (GANs). Besides of the realism, more accurate control on the properties of face images. While sketches convey the desired shapes, attributes describe appearance. However, it remains challenging to jointly exploit sketches and attributes, which are in different modalities, to generate high-resolution photorealistic face images. In this paper, we propose a novel joint sketch-attribute learning approach to synthesize photo-realistic face images with conditional GANs. A hybrid generator is proposed to learn a unified embedding of shape from sketches and appearance from attributes for synthesizing images. We propose an attribute modulation module, which transfers user-preferred attributes to reinforce sketch representation with appearance details. Using the proposed approach, users could flexibly manipulate the desired shape and appearance of synthesized face images with fine-grained control. We conducted extensive experiments on the CelebA-HQ dataset [16]. The experimental results have demonstrated the effectiveness of the proposed approach.
Preserving Ordinal Consensus: Towards Feature Selection for Unlabeled Data

To better pre-process unlabeled data, most existing feature selection methods remove redundant and noisy information by exploring some intrinsic structures embedded in samples. However, these unsupervised studies focus too much on the relations among samples, totally neglecting the feature-level geometric information. This paper proposes an unsupervised triplet-induced graph to explore a new type of potential structure at feature level, and incorporates it into simultaneous feature selection and clustering. In the feature selection part, we design an ordinal consensus preserving term based on a triplet-induced graph. This term enforces the projection vectors to preserve the relative proximity of original features, which contributes to selecting more relevant features. In the clustering part, Self-Paced Learning (SPL) is introduced to gradually learn from ‘easy’ to ‘complex’ samples. SPL alleviates the dilemma of falling into the bad local minima incurred by noise and outliers. Specifically, we propose a compelling regularizer for SPL to obtain a robust loss. Finally, an alternating minimization algorithm is developed to efficiently optimize the proposed model. Extensive experiments on different benchmark datasets consistently demonstrate the superiority of our proposed method.
A new hand gestures recognition system

&lt;p&gt;Talking about gestures makes us return to the historical beginning of human communication, because, in fact, there is no language completely free of gestures. People cannot communicate without gestures. Any action or movement without gestures is free of real feelings and cannot express the thoughts. The purpose from any hand gestures recognition system is to recognizes the hand gesture and used it to transfer a certain meaning or for computer control or and device. Our paper introduced a low cost system to recognize the hand gesture in real-time. Generally, the system divided into five steps, one to image acquisition, second to pre-processing the image, third for detection and segmentation of hand region, four to features extraction and five to count the numbers of fingers and gestures recognition. The system has coded by Python language, PyAutoGUI library, OS Module of Python and the OpenCV library.&lt;/p&gt;
Results of the Ontology Alignment Evaluation Initiative 2019

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity (from simple thesauri to expressive OWL ontologies) and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2019 campaign offered 11 tracks with 29 test cases, and was attended by 20 participants. This paper is an overall presentation of that campaign.
Classifying Obstacles and Exploiting Class Information for Humanoid Navigation Through Cluttered Environments

Humanoid robots are often supposed to share their workspace with humans and thus have to deal with objects used by humans in their everyday life. In this article, we present our novel approach to humanoid navigation through cluttered environments, which exploits knowledge about different obstacle classes to decide how to deal with obstacles and select appropriate robot actions. To classify objects from RGB images and decide whether an obstacle can be overcome by the robot with a corresponding action, e.g., by pushing or carrying it aside or stepping over or onto it, we train and exploit a convolutional neural network (CNN). Based on associated action costs, we compute a cost grid containing newly observed objects in addition to static obstacles on which a 2D path can be efficiently planned. This path encodes the necessary actions that need to be carried out by the robot to reach the goal. We implemented our framework in the Robot Operating System (ROS) and tested it in various scenarios with a Nao robot as well as in simulation with the REEM-C robot. As the experiments demonstrate, using our CNN, the robot can robustly classify the observed obstacles into the different classes and decide on suitable actions to find efficient solution paths. Our system finds paths also through regions where traditional motion planning methods are not able to calculate a solution or require substantially more time.
Incorporating finer acoustic phonetic features in lexicon for Hindi language speech recognition

Lexicon, used in speech recognition, is the list of words and corresponding pronunciation using a standard acoustic notation. Currently ILSL12 phone-set is widely used for Indian Language speech recognition. However, it has limitations to represent features of the speech like, voicing, fricatives, etc. that are found in Indian Languages. This paper addresses this limitation by considering the voiced and unvoiced features for Hindi speech recognition by incorporating finer representations at the time of lexicon expansion. The approach is tested for Hindi word recognition and has shown significant improvement in WER.