Reviewerly/exHarmony huggingface dataset:
Non-linear Diffusion Models for Despeckling of Images: Achievements and Future Challenges

Speckle noise is a principal and unavoidable source of visual degradation in several real world images obtained from coherent imaging systems such as ultrasound, SAR, and laser. Over the last few decades, several mathematical models have been used to study despeckling and have influenced policy. This paper review and compare the partial differential equation-based non-linear diffusion models for despeckling of digital grey scale natural and real images, available in the literature. First, we present a detailed study of different classes of partial differential equation-based models namely, logarithmic transform, anisotropic diffusion, and total variation based models, which have different assumptions about noise removal and explore the effect of treatments. Then, to test the effectiveness of different discussed classes of PDE-based models, a set of natural images with different geometric regions, three real ultrasound images and two real SAR images are used. Also, to assess the quality of considered test images after despeckling using numeric metrics, three with-reference and two without-reference indexes are used in the experimental study. A few experiments with natural and real images reveal that the filters based on logarithmic transformation are cost effective with low noise suppression capability. Whereas, the anisotropic diffusion and total variation-based approaches show the good filtering performance with high computational cost. Therefore, rigorous qualitative and quantitative studies confirm that the seemingly subtle variation in model assumptions can have remarkable impact on despeckling.
Information Security Methods—Modern Research Directions

In Tomsk University of Control Systems and Radioelectronics (TUSUR) one of the main areas of research is information security. The work is carried out by a scientific group under the guidance of Professor Shelupanov. One of the directions is the development of a comprehensive approach to assessing the security of the information systems. This direction includes the construction of an information security threats model and a protection system model, which allow to compile a complete list of threats and methods of protection against them. The main directions of information security tools development are dynamic methods of biometrics, methods for generating prime numbers for data encryption, steganography, methods and means of data protection in Internet of Things (IoT) systems. The article presents the main results of research in the listed areas of information security. The resultant properties in symmetric cryptography are based on the properties of the power of the generating functions. The authors have obtained symmetric principles for the development of primality testing algorithms, as discussed in the Appendix.
SeVEP: Secure and Verifiable Electronic Polling System

Electronic polling systems promise benefits to voters such as accessibility and convenience that enable them to cast their votes at any time, from any Internet-connected computing device anywhere in the world. However, unlike traditional paper-based voting systems, an e-polling system introduces several security risks such as privacy of vote, unlinkability of a voter, voter coercion, secrecy of partial election results, verifiability, and poll integrity. The authenticity of a voter is another security concern, i.e., a voter must be identified through an authentication mechanism that prevents voting of unauthorized voters or multiple voting from authorized voters. Another security concern is the manipulation of votes by an infected (e.g., virus, malware, and so on) voting device. Since the voters use their personal devices to cast votes in an unsupervised environment, a malware-hosted device could make unauthorized modifications to the voter's voting choices. Many e-voting systems have been proposed, however, to date, all these schemes either fail to provide all the required security properties or are not practically feasible on light-weight computing devices. In this paper, we present a secure and verifiable polling system, SeVEP, that employs well-known cryptographic primitives to provide vote and voter's privacy, and poll integrity, confirms the identity of voters through a multifactor authentication scheme, enables multiple voting within the allowed polling period, prevents double voting, and achieves verifiability and uncoercibility in the presence of untrusted voting device. The security, performance, and comparative analysis in terms of security properties and cryptographic costs show that SeVEP is secure, verifiable, and practical e-polling system.
Unsupervised Video Matting via Sparse and Low-Rank Representation

A novel method, unsupervised video matting via sparse and low-rank representation, is proposed which can achieve high quality in a variety of challenging examples featuring illumination changes, feature ambiguity, topology changes, transparency variation, dis-occlusion, fast motion and motion blur. Some previous matting methods introduced a nonlocal prior to search samples for estimating the alpha matte, which have achieved impressive results on some data. However, on one hand, searching inadequate or excessive samples may miss good samples or introduce noise; on the other hand, it is difficult to construct consistent nonlocal structures for pixels with similar features, yielding video mattes with spatial and temporal inconsistency. In this paper, we proposed a novel video matting method to achieve spatially and temporally consistent matting result. Toward this end, a sparse and low-rank representation model is introduced to pursue consistent nonlocal structures for pixels with similar features. The sparse representation is used to adaptively select best samples and accurately construct the nonlocal structures for all pixels, while the low-rank representation is used to globally ensure consistent nonlocal structures for pixels with similar features. The two representations are combined to generate spatially and temporally consistent video mattes. We test our method on lots of dataset including the benchmark dataset for image matting and dataset for video matting. Our method has achieved the best performance among all unsupervised matting methods in the public alpha matting evaluation dataset for images.
Singularity mapping of bulk leach extractable gold and −80# stream sediment geochemical data in recognition of gold and base metal mineralization footprints in Biga Peninsula South, Turkey

Stream sediments remain the preferred geochemical sampling media for regional mineral exploration programs in most parts of the world where there is a well-defined drainage system. In Au exploration the analysis of either Bulk Leach Extractable Gold (BLEG) or acid-extractable Au in the −80# (<180 μm) stream sediment fraction are the two most common approaches. Western Turkey contains a variety of mineral deposit types and geological settings. Using a geochemical database derived mainly from company exploration programs, a study is made on the efficiency of singularity mapping technique in detecting the known mineral deposits or prospects. The composite media of samples are representative of sampling locations but also are extremely diluted. Therefore, recognition and mapping of significant geochemical anomalies of composite media still remains a challenge. In this study, geochemical assay results were subjected to multifractal analyses to detect gold geochemical anomalies, which are related to gold mineralization. In this regard, a singularity mapping technique was applied to enhance the single element geochemical signatures of BLEG sand −80# stream sediment data. Since the singularity mapping technique does not define threshold values for delineation of anomalies, number-size (N-S) and concentration-area (C-A) fractal models were applied to delimit the geochemical anomalies as target areas for further exploration. A hundred percent of the known gold deposits and prospects discovered in the study area are delineated by BLEG Au stream sediment anomalies by N-S and C-A multifractal modeling. Moreover, 89% of known gold prospects of the study area are delineated within the recognized anomalous zones of Au in −80# stream sediment data. The comparison of the delineated geochemical anomalies of BLEG and −80# stream sediment with those of rock chip samples demonstrated the efficiency of the applied methodology.
A highly robust and secure image watermarking based on classification and visual cryptography

In this paper a new robust image watermarking algorithm based on blocks classification and visual cryptography (VC) is presented. First the original image is decomposed into non-overlapping blocks. Then, we use canny edge detection and the support vector machine (SVM) classification method to categorize these blocks into smooth and non-smooth classes. The VC technique is used to generate two image shares: A master share that is constructed according to the block classification results and an owner share generated by using the master share together with a binary watermark. To verify the ownership of the image, the watermark can be retrieved by stacking the master share and the owner share. By skipping blocks which are not robust against attacks, the robustness of our proposed watermarking method is significantly improved. Our method is completely imperceptible because the watermark pattern is concealed without modifying the original host image.
A Multi-Agent Architecture for Data Analysis

ActoDatA (Actor Data Analysis) is an actor-based software library for the development of distributed data mining applications. It provides a multi-agent architecture with a set of predefined and configurable agents performing the typical tasks of data mining applications. In particular, its architecture can manage different users’ applications; it maintains a high level of execution quality by distributing the agents of the applications on a dynamic set of computational nodes. Moreover, it provides reports about the analysis results and the collected data, which can be accessed through either a web browser or a dedicated mobile APP. After an introduction about the actor model and the software framework used for implementing the software library, this article underlines the main features of ActoDatA and presents its experimentation in some well-known data analysis domains.
Vision-Based 3D Modeling of Unknown Dynamic Environments for Real-Time Humanoid Navigation

In order to achieve real autonomy, robots have to be able to navigate in completely unknown environments. Due to the complexity of computer vision algorithms, almost every approach for robotic navigation is either based on previous knowledge of the environment, such as markers or as resulting from learning methods, or makes strong simplifying assumptions about it (height-map representations, static scenarios). While showing impressive success in certain applications, these approaches limit the potential of legged robots to achieve the amazing flexibility of humans in more complex environments. In this work, we present a strategy for full 3D vision processing that is able to handle changing, dynamic environments. These are modeled using 3D geometries that are processed in real-time by the motion planner of our biped robot Lola for avoiding moving obstacles and walking over platforms. In order to allow for a more intuitive development of such systems in the future, we present tools for visualization including two mixed reality applications using both an external camera and Microsoft’s HoloLens. We validate our system in simulations and experiments with our full-size humanoid robot Lola and publish our framework open source for the benefit of the community.
Towards Predicting a Realisation of an Information Need based on Brain Signals

The goal of Information Retrieval (IR) systems is to satisfy searchers' Information Need (IN). Our research focuses on next-generation IR engines, which can proactively detect, identify, and serve INs without receiving explicit queries. It is essential, therefore, to be able to detect when INs occur. Previous research has established that a realisation of INs physically manifests itself with specific brain activity. With this work we take the next step, showing that monitoring brain activity can lead to accurate predictions of a realisation of IN occurrence. We have conducted experiments whereby twenty-four participants performed a Q/A Task, while their brain activity was being monitored using functional Magnetic Resonance Imaging (fMRI) technology. The questions were selected and developed from the TREC-8 and TREC 2001 Q/A Tracks. We present two methods for predicting the realisation of an IN, i.e. Generalised method (GM) and Personalised method (PM). GM is based on the collective brain activity of all twenty-four participants in a predetermined set of brain regions known to be involved in representing a realisation of INs. PM is unique to each individual and employs a 'Searchlight' analysis to locate brain regions informative for distinguishing when a “specific” user realises an information need. The results of our study show that both methods were able to predict a realisation of an IN (statistically) significantly better than chance. Our results also show that PM (statistically) significantly outperformed GM in terms of prediction accuracy. These encouraging findings make the first fundamental step towards proactive IR engines based on brain signals.
Developing and validating a methodology for crowdsourcing L2 speech ratings in Amazon Mechanical Turk

Abstract Researchers have increasingly turned to Amazon Mechanical Turk (AMT) to crowdsource speech data, predominantly in English. Although AMT and similar platforms are well positioned to enhance the state of the art in L2 research, it is unclear if crowdsourced L2 speech ratings are reliable, particularly in languages other than English. The present study describes the development and deployment of an AMT task to crowdsource comprehensibility, fluency, and accentedness ratings for L2 Spanish speech samples. Fifty-four AMT workers who were native Spanish speakers from 11 countries participated in the ratings. Intraclass correlation coefficients were used to estimate group-level interrater reliability, and Rasch analyses were undertaken to examine individual differences in rater severity and fit. Excellent reliability was observed for the comprehensibility and fluency ratings, but indices were slightly lower for accentedness, leading to recommendations to improve the task for future data collection.
Analysis of an evolutionary algorithm for complex fuzzy cognitive map learning based on graph theory metrics and output concepts

The fuzzy cognitive map (FCM) is an effective tool for modeling dynamic decision support systems. It describes the analyzed phenomenon in the form of key concepts and the causal connections between them. The main aspects of the building of the FCM model are: concepts selection, determining the output concepts, criterion selection, and determining the relationships between concepts. It is usually based on expert knowledge. The main goal of the paper is to define the optimal in some sense FCM structure through the introduction of the notion of output concepts and minimizing the number of concepts and connections between them. The proposed approach allows for: (1) the selection of key concepts based on graph theory metrics and determining the connections between them; (2) the determination of the criterion of learning based on output concepts and fitting the learning process to the analyzed problem. A simulation analysis was done with the use of synthetic and real-life data. Experiments confirm that the proposed approach improves the learning process compared to the standard approaches.
Super-resolution of PET image based on dictionary learning and random forests

Positron emission tomography (PET) is an imaging technique for nuclear medicine and clinical diagnosis that is widely used in oncology and clinical medicine. However, PET has limitations related to its lower resolution than other medical imaging modalities, such as X-ray computed tomography (CT) and magnetic resonance imaging (MRI). In this paper, we propose an improved super-resolution (SR) method based on dictionary learning and random forests for the PET system to improve the resolution of PET images. First, we process the acquired high-resolution (HR) PET images ourselves to obtain multiple types of low-resolution (LR) PET images. Next, we directly train the mapping from LR to HR PET patches using random forests. Experimental results based on both clinical and medical images show that the proposed method is effective in improving PET image quality in terms of numerical criteria and visual results. The proposed method can minimize noise and artifacts without blurring the edges of the PET image, which can preserve important structural details, such as those indicating lesions. Therefore, the proposed method has excellent potential for applications in actual clinical and medical systems.
Advanced community question answering by leveraging external knowledge and multi-task learning

Community question answering (CQA) is an important but challenging task. Meantime, as the theory of deep learning develops, remarkable progress has been made by deep neural networks. This paper studies an advanced deep neural network that not only uses external knowledge to learn better representations of questions and answers but also improves representation learning by considering question categorization as an auxiliary task. Specifically, we propose a novel Multi-task and Knowledge enhanced Multi-head Interactive Attention network for Community Question Answering (MKMIA-CQA). It contains a document modeling module responsible for utilizing external commonsense knowledge to help identify background information (entity mentions and their relations) and filter out noise information from the long text which has complicated semantic and syntactic structures. Moreover, the model is trained in a multi-task manner. It regards community question answering as the primary task and question categorization as the auxiliary task, which aims to learn a category-aware encoder and improve the quality of locating the salient information of a long question. The experimental results on three widely used CQA datasets demonstrate that our model achieves impressive results compared to other strong competitors.
Medical big data analysis: preserving security and privacy with hybrid cloud technology

Globally, a big data analytics is a significant technology which is employed in various business areas including welfare medical field. The ultimate objective of proposed work has been given a secure healthcare private data in the cloud by using fog computing technology. Additionally, a tri-party one-round authenticated key-agreement protocol can be modeled based on bilinear pairing cryptography that can generate a session key among participants and communicate securely. Finally, private healthcare data are accessed and stored securely via implementation process of using decoy technique. In decoy files, when an attacker is detected accessing the system data, proposed method offers, decoy files are retrieved from starting to ensure data security. Moreover, furnishing double security by encrypting the original file when attacker recognizes attacker and him/her dealing with decoy gallery; they must figure out how to decode original gallery data. Hence, it is able to provide and ensures that patients MBD are 100% secure and shortens the process. There is no need to afraid about if the user is an attacker, since it provides decoy big data gallery to any user while keeping original data hidden, only made available to a legitimate authenticated user by successful verification.
Reservoir Computing Based Echo State Networks for Ventricular Heart Beat Classification

The abnormal conduction of cardiac activity in the lower chamber of the heart (ventricular) can cause cardiac diseases and sometimes leads to sudden death. In this paper, the author proposed the Reservoir Computing (RC) based Echo State Networks (ESNs) for ventricular heartbeat classification based on a single Electrocardiogram (ECG) lead. The Association for the Advancement of Medical Instrumentation (AAMI) standards were used to preprocesses the standardized diagnostic tool (ECG signals) based on the interpatient scheme. Despite the extensive efforts and notable experiments that have been done on machine learning techniques for heartbeat classification, ESNs are yet to be considered for heartbeat classification as a is fast, scalable, and reliable approach for real-time scenarios. Our proposed method was especially designed for Medical Internet of Things (MIoT) devices, for instance wearable wireless devices for ECG monitoring or ventricular heart beat detection systems and so on. The experiments were conducted on two public datasets, namely AHA and MIT-BIH-SVDM. The performance of the proposed model was evaluated using the MIT-BIH-AR dataset and it achieved remarkable results. The positive predictive value and sensitivity are 98.98% and 98.98%, respectively for the modified lead II (MLII) and 98.96% and 97.95 for the V1 lead, respectively. However, the experimental results of the state-of-the-art approaches, namely the patient-adaptable method, improved generalization, and the multiview learning approach obtained 92.8%, 87.0%, and 98.0% positive predictive values, respectively. These obtained results of the existing studies exemplify that the performance of this method achieved higher accuracy. We believe that the improved classification accuracy opens up the possibility for implementation of this methodology in Medical Internet of Things (MIoT) devices in order to bring improvements in e-health systems.
Tectonic discrimination of olivine in basalt using data mining techniques based on major elements: a comparative study from multiple perspectives

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Photo sleuth

Identifying people in historical photographs is important for preserving material culture, correcting the historical record, and creating economic value, but it is also a complex and challenging task. In this paper, we focus on identifying portraits of soldiers who participated in the American Civil War (1861-65), the first widely-photographed conflict. Many thousands of these portraits survive, but only 10--20% are identified. We created Photo Sleuth, a web-based platform that combines crowdsourced human expertise and automated face recognition to support Civil War portrait identification. Our mixed-methods evaluation of Photo Sleuth one month after its public launch showed that it helped users successfully identify unknown portraits and provided a sustainable model for volunteer contribution. We also discuss implications for crowd-AI interaction and person identification pipelines.