Reviewerly/exHarmony huggingface dataset:
Maxwell®: An Unsupervised Learning Approach for 5P Medicine.

In the 5P medicine (Personalized, Preventive, Participative, Predictive and Pluri-expert), the general trend is to process data by displacing the barycenter of the information from hospital centered systems to the patient centered ones through his personal medical records. Today, the use of artificial intelligence for supporting this transition shows real limitations in its implementation in operational practice, both at the level of patient care, but also in the general daily life of the health professional, because of the medico-legal imperatives induced by the promises of the '5P medicine'. In this paper, we propose to fill this gap by introducing an original artificial intelligence platform, named Maxwell, which follows an unsupervised learning approach in line with the medico-legal imperatives of the '5P medicine'. We describe the functional platform characteristics and illustrate them by two examples of clustering in genomics and magnetic resonance imaging.
Building digital competence in professional education

In the next seven years, the Russian economy will have to switch to digital technology and digital product. The purpose of the article is to establish general recommendations to develop and implement digital competencies. The subject of the research is the set of necessary digital competencies and their ranking. To make it easier for people to adapt to the digital economy, the authors of the article conducted a survey among the chief executives of leading enterprises in Nizhnevartovsk to predict which digital competencies need to be taken into account in professional training. In addition, the research revealed additional features that a person should possess, e.g. the information consumption culture and decisiveness. The modern Russian professional education framework is setting a new educational paradigm for students and lifelong learning. This includes fundamental changes in the professional objectives of higher education. Therefore, the training must adapt to the new contextual requirements. The article provides reflections on building a profile of digital competencies for areas of higher education training that correspond to the current context and new scenario, including those that are a cross-cutting axis for other professional competencies that become relevant in the educational domain. Our research and analysis revealed areas for the further development of digital competencies, as well as three basic principles that are crucial for the successful design and advancement of new programmes in professional education. Standardized mass education in the field of digital economy is no longer relevant. Now taxonomy defines the skills that need to be included into the digital economy through a wide range of professions. This article provides a new insight into the role of competence transition from education to the labor market. To understand today’s demand for new skills and elaborate on how it might change over the next 5-10 years, we recruited employers to identify the competencies that they consider essential.
A Novel Radar Signals Sorting Method-Based Trajectory Features

When several foreign fighters with the same type enter detection range, the electronic warfare (EW) receivers will intercept many the same type radar emitter signals. If the intercepted pulse is processed by the traditional sorting methods, the number of emitters cannot be identified. The main reason is that the same type of radar has similar parameters. It will cause a devastating influence on subsequent strategic decisions. A novel sorting method based on the trajectory features is proposed to solve the aforementioned problems. First, the trajectory features of the intercepted pulse signal are extracted. Then, the segmentation method is utilized to preprocess the signals, which enhances the computing efficiency and improves the sorting accuracy. Meanwhile, a prediction framework based on long short-term memory (LSTM) recurrent neural network is established to forecast pulses. Finally, the radar stagger pulses are sorted by forecast pulses. The simulation results show that the proposed method can recognize the number of emitters and achieve high sorting accuracy. It provides a new idea for the radar signals sorting of the same type.
Infection level identification for leukemia detection using optimized Support Vector Neural Network

Leukemia is the abnormal and uncontrolled development of the white blood cells, known as leukocytes, in the blood. The manual methods used for counting the blast cells have some demerits, and so automatic method must be employed. This paper proposes the Salp Swarm integrated Dolphin Echolocation-based Support Vector Neural Network (SSDE-SVNN) classifier to detect leukemia in its early stages. The pre-processed blood smear image is subjected to segmentation with the use of LUV transformation and Adaptive thresholding. The features, such as area, shape, texture, and empirical mode decomposition are extracted from the segments. The proposed classifier is used for the counting of blast cells based on the extracted features. The accuracy, specificity, and sensitivity of the proposed classifier are obtained as 0.97, 0.97, and 1, respectively, and the Mean Square Error (MSE) is noted as 0.1272.
Efficient method for breast cancer classification based on ensemble hoffeding tree and naïve Bayes

&lt;p&gt;&lt;span&gt;The most dangerous type of cancer suffered by women above 35 years of age is breast cancer. Breast Cancer datasets are normally characterized by missing data, high dimensionality, non-normal distribution, class imbalance, noisy, and inconsistency. Classification is a machine learning (ML) process which has a significant role in the prediction of outcomes, and one of the outstanding supervised classification methods in data mining is Naives Bayess Classification (NBC). Naïve Bayes Classifications is good at predicting outcomes and often outperforms other classifications techniques. Ones of the reasons behind this strong performance of NBC is the assumptions of conditional Independences among the initial parameters and the predictors. However, this assumption is not always true and can cause loss of accuracy. Hoeffding trees assume the suitability of using a small sample to select the optimal splitting attribute. This study proposes a new method for improving accuracy of classification of breast cancer datasets. The method proposes the use of Hoeffding trees for normal classification and naïve Bayes for reducing data dimensionality.&lt;/span&gt;&lt;/p&gt;
Single Vehicle Localization and Routing in GPS-Denied Environments Using Range-Only Measurements

Path planning algorithms for unmanned aerial or ground vehicles rely on Global Positioning System (GPS) information for localization in many surveillance and reconnaissance applications.However, disruption of GPS signals, by intention or otherwise, can render these algorithms ineffective.This paper provides a way of addressing this issue by leveraging range information from additionally placed stationary objects in the environment called Landmarks (LMs).The placement of LMs and the route followed by the vehicle is posed as an integer program such that the total travel and LM placement cost is minimized.The proposed formulation of the optimization problem also allows for a limited field-of-view of the sensor on-board the vehicle.For instances that are hard to solve for optimal solutions using the integer program, we present two fast heuristics to find good feasible solutions.We provide a systematic framework and algorithms for the problem, and evaluate the system using numerical, simulation and experimental results.
Exploring pervasive displays for cemeteries and memorial sites

Abstract This paper addresses graveyards as a context for designing interactive technology, especially pervasive displays and the presentation of information related to graves and the dead. We present our research containing three user studies: a focus group–based study on perceptions of different display technologies in the cemetery context, evaluation of a gravestone display prototype, and a user study with a graveyard navigator prototype. Whereas, HCI research surrounding death has so far largely focused on an individual’s digital remains, our focus is on the physical graveyard setting. We contribute to understanding the potential impacts and opportunities of interactive technology in this design context. Our salient findings highlight the cultural sensitivity and importance of dignity related to the context, unobtrusiveness of the technology, and concerns for privacy and social acceptability. Concepts taking into account these design aspects were of interest for those researching family histories, and visitors seeking improved navigation for the graveyard.
A unified principled framework for resampling based on pseudo-populations: Asymptotic theory

In this paper, a class of resampling techniques for finite populations under π ps sampling design is introduced.The basic idea on which they rest is a two-step procedure consisting in: (i) constructing a "pseudopopulation" on the basis of sample data; (ii) drawing a sample from the predicted population according to an appropriate resampling design.From a logical point of view, this approach is essentially based on the plugin principle by Efron, at the "sampling design level".Theoretical justifications based on large sample theory are provided.New approaches to construct pseudo populations based on various forms of calibrations are proposed.Finally, a simulation study is performed.
Digital Criminology: the Mmethod of Digital Profiling of an Unidentified Criminal's Behavior

The authors examine the specific features of the digital environment to analyze contemporary approaches to the system of instruments of studying illegal actions in cyberspace. They state that law enforcement bodies should adapt key characteristics of the digital environment to the accomplishment of investigation tasks. The authors analyze the possibilities offered by digital profiling and modeling of the digital profile (portrait) of an unidentified criminal through mathematical methods of modeling and prediction in investigating and solving serial crimes, including cybercrimes. An extensive review of Russian and foreign publications is used to study the evolution of scientific ideas regarding the profiling method, which is the basis for the digital profiling of the behavioral model of an unidentified criminal in the digital environment. It is stated that none of the branches of criminal law, including criminology and criminalistics, could alone solve the interdisciplinary problem of the investigation and detection of crimes in the digital environment. The authors prove that it is necessary to integrate the knowledge of these branches and to conduct interdisciplinary research involving experts, i.e. to duly streamline the organization of those activities that together make up the investigation and detection of crimes. Based on the content of the concept «modus operandi», which lies at the heart of building an abstract model of criminal behavior, they conclude that it could be used to investigate and solve crimes in the digital environment and determine the specific features of the content of its structural elements. The comparative analysis of the contents of the key stages of profiling is used to prove the expediency of employing the whole range of logical and mathematical methods of analysis to process and analyze criminological information, which leads to the necessity of both critically reviewing them and finding ways to go beyond the traditional approaches. The authors describe the essence of the mathematical extrapolation method, which is most commonly used in criminology for the quantitative analysis of knowledge regarding objects, phenomena, processes, as well as the possibility of using it in digital profiling. As a result of this research based on the systemic approach, the authors state the objective character of links between the traditional and the digital profiling, point out the existing links and regularities, which allow them to reduce the essence of the examined phenomena to building a model through the recreation, in the process of investigation, of the mental trace pattern and then using it to find the guilty person.
Orienting the Development of Crime Analysis Processes in Police Organisations Covering the Digital Transformations of Fraud Mechanisms

A significant, and likely dominant, proportion of fraud is now conducted online. Police struggle to integrate this emerging reality into their processes, while expectations of this institution are high. These types of cybercrimes alter the volume and complexity of problems compared to how they previously manifested and require profound transformations of crime analysis methods to address them proactively. These developments face many difficulties, such as the quality of accessible data, the lack of existing analytical models and the need to increase police knowledge of fraud mechanisms within every level of organisations. We suggest methods to overcome these obstacles, which consist of implementing an approach integrating theories from various fields in criminology and forensic intelligence to examine the digital transformations of certain criminal processes. We take, as an example, how a generic script expressing the anatomy of an existing type of fraud can be used to interpret their new digital forms. This modelling activity both provides new insight into specific frauds and highlights relevant dimensions that are useful in orienting the development of crime analysis systems.
A Blockchain-Based Approach To Enable Remote Sensing Trusted Data

Remote sensing considerably benefits from the fusion of open data from different sources, including far-range sensors mounted on satellites and short-range sensors on drones or Internet of Things devices. Open data is an emerging philosophy attracting an increasing number of data owners willing to share. However, most of the data owners are unknown and thus, untrustable, which makes shared data likely unreliable and possibly compromising associated outcomes. Currently, there exist tools that distribute open data, acting as intermediaries connecting data owners and users. However, these tools are managed by central authorities that set rules for data ownership, access, and integrity, limiting data owners and users. Therefore, a need emerges for a decentralized system to share and retrieve data without intermediaries limiting participants. Here, we propose a blockchain-based system to share and retrieve data without the need for a central authority. The proposed architecture (i) allows sharing data, (ii) maintains the data history (origin and updates), and (iii) allows retrieving and evaluating the data adding trustworthiness. To this end, the blockchain network enables the direct connection of data owners and users. Furthermore, blockchain automatically interacts with participants and keeps a transparent record of their actions. Hence, blockchain provides a decentralized database that enables trust among the participants without a central authority. We analyzed the potentials and critical issues of the architecture in a remote sensing use case of precision farming. The analysis shows that participants benefit from the properties of the blockchain in providing trusted data for remote sensing applications.
Parallel GraphBLAS with OpenMP

SuiteSparse:GraphBLAS is a complete implementation of the GraphBLAS standard. It provides a powerful and expressive framework for creating graph algorithms based on the elegant mathematics of sparse matrix operations on a semiring. Algorithms written with the GraphBLAS achieve high performance with minimal development time. Multithreaded parallelism through OpenMP provides additional speedup, which we illustrate on a 20-core Intel® Xeon® E5-2698 CPU system when solving various problems (triangle counting, k-truss, breadth-first search, Bellman-Ford, local clustering coefficient, and a sparse deep neural network problem). This wide variety of algorithms illustrates the expressiveness of the GraphBLAS API to create new graph algorithms. We present performance results with these algorithms on a set of large real-world graphs, using the newly developed Suite-Sparse:GraphBLAS v3.0.1.
Nonlinear extension of the quantum dynamical semigroup

In this paper we consider deterministic nonlinear time evolutions satisfying so called convex quasi-linearity condition. Such evolutions preserve the equivalence of ensembles and therefore are free from problems with signaling. We show that if family of linear non-trace-preserving maps satisfies the semigroup property then the generated family of convex quasi-linear operations also possesses the semigroup property. Next we generalize the Gorini-Kossakowski-Sudarshan-Lindblad type equation for the considered evolution. As examples we discuss the general qubit evolution in our model as well as an extension of the Jaynes-Cummings model. We apply our formalism to spin density matrix of a charged particle moving in the electromagnetic field as well as to flavor evolution of solar neutrinos.
Integrating Sentiment Analysis in Recommender Systems

Customer product reviews play an important role in the customer’s decision to purchase a product or use a service. Providing a useful suggestion of products to online users to increase their consumption on websites is the goal of many companies nowadays. In this paper, we propose a recommender system based on sentiment analysis. The system is built by integrating sentiment analysis to recommender system in order to generate the most accurate. We use hybrid deep learning method CNN-LSTM for sentiment analysis based on vector of words in the customer product reviews. The result in the sentiment analysis is used to combine the neighbor’s item ratings to produce a prediction value for the target user. This helps the recommender system to generate efficient recommendations for that user. We do experiment in Amazon food review dataset. The proposed model shows interesting results on the impact of integrating sentiment analysis in the recommender systems.
Secure Online Examination System for e-learning

As advancements in information and communication technology increases rapidly in our day to day life, the e-learning system has gained more attention in the educational institutions. Many educational institutions try to progress from pen-paper examination to online examination for many reasons, including personalized environment, secured system, and accurate evaluation. This research work focuses on a simple scheme for mutual authentication between the student and the server and secure delivery of question paper from the server. The experimental results shown in this research work are based on a set of survey questions given for students and instructors. The results are promising towards the use of the proposed system during the examination.
AVATAR - Machine Learning Pipeline Evaluation Using Surrogate Model

The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline composition and optimisation of these methods requires a tremendous amount of time that prevents them from exploring complex pipelines to find better predictive models. To further explore this research challenge, we have conducted experiments showing that many of the generated pipelines are invalid, and it is unnecessary to execute them to find out whether they are good pipelines. To address this issue, we propose a novel method to evaluate the validity of ML pipelines using a surrogate model (AVATAR). The AVATAR enables to accelerate automatic ML pipeline composition and optimisation by quickly ignoring invalid pipelines. Our experiments show that the AVATAR is more efficient in evaluating complex pipelines in comparison with the traditional evaluation approaches requiring their execution.
Predictive inference with Fleming–Viot-driven dependent Dirichlet processes

We consider predictive inference using a class of temporally dependent Dirichlet processes driven by Fleming–Viot diffusions, which have a natural bearing in Bayesian nonparametrics and lend the resulting family of random probability measures to analytical posterior analysis. Formulating the implied statistical model as a hidden Markov model, we fully describe the predictive distribution induced by these Fleming–Viot-driven dependent Dirichlet processes, for a sequence of observations collected at a certain time given another set of draws collected at several previous times. This is identified as a mixture of Pólya urns, whereby the observations can be values from the baseline distribution or copies of previous draws collected at the same time as in the usual Pólya urn, or can be sampled from a random subset of the data collected at previous times. We characterize the time-dependent weights of the mixture which select such subsets and discuss the asymptotic regimes. We describe the induced partition by means of a Chinese restaurant process metaphor with a conveyor belt, whereby new customers who do not sit at an occupied table open a new table by picking a dish either from the baseline distribution or from a time-varying offer available on the conveyor belt. We lay out explicit algorithms for exact and approximate posterior sampling of both observations and partitions, and illustrate our results on predictive problems with synthetic and real data.
A group-key-based sensitive attribute protection in cloud storage using modified random Fibonacci cryptography

Abstract Cloud computing is an eminent technology for providing a data storage facility with efficient storage, maintenance, management and remote backups. Hence, user data are shifted from customary storage to cloud storage. In this transfer, the sensitive attributes are also shifted to cloud storage with high-end security. Current security techniques are processed with high encryption time and provide identical security of entire data with single key dependent. These processes are taking high computational time and leaks entire information if the key is hacked. The proposed Group Key Based Attribute Encryption using Modified Random Fibonacci Cryptographic (MRFC) technique rectifies these issues. Instead of machine learning technique, data owner preference-based attributes segregation is used to divide an input dataset into sensitive and non-sensitive attribute groups. Based on inter-organization usage and data owner’s willingness, sensitive attribute is divided into ‘n + 1′ subgroups and each subgroup is encrypted by ‘n + 1’ group keys. The encrypted sensitive subgroups are merged with non-sensitive attributes and uploaded into a private cloud. The novelties of this paper are, (1) data owner preferred sensitive attribute classification instead of machine learning algorithms, (2) sensitive attribute encryption instead of entire attributes, (3) To reduce encryption time without compromising data owner privacy, (4) To decrypt and access the required subgroup instead of the entire attribute. Our experimental results show that, the proposed method takes minimal processing time, better classification accuracy and minimal memory space with high security to selected attributes as compared to existing classification and security techniques. Hence, sensitive data security and privacy is achieved with minimal processing cost.