Reviewerly/exHarmony huggingface dataset:
Problems of Drug Addiction Among Student-Age Population: Analysis and Ways of Counteraction

The paper deals with the problem of drug addiction in the student environment. This work is a sociological study. Students` attitude to narcotic and psychotropic substances is under examination. The method of anonymous questionnaire survey was used to collect empirical data among the students of technical university. The data on the evaluation of drug use problem among young people was obtained. An analysis of the results was performed.
AI Embedded Transparent Health and Medicine System

Along with the rapid exploration of intelligent system, AI methods have been applied in health and medicine fields to manage the fast growing medical data in recent years. Scientists and engineers started their first attempts. Medical history is collected and studied for automated diagnose, skin cancer images are deeply learned for auto judgment and classification [], lung nodules are segmented automatically [], and the future of medicine is debated in []. In this paper, we set up the transparent health and medicine AI system for the future, based on etiological analysis, construct total resolution network, provide accurate orientation and precise treatment, applying intelligent algorithms.
Posterior Cramér-Rao Lower Bound for Multiple Passive Sensors in an Uncertain Ocean Environment

In this paper, our study is motivated by the fact that it is not always clear what the placement of the multiple passive sensors giving the best tracking performance for the underwater targets of interest might be. To account for the issue, posterior Cramér-Rao lower bound (PCRLB) is utilized, which provides a measure of the optimal achievable accuracy of the target state estimation. To derive the recursive Fisher information matrix (FIM) and PCRLB for multisensor multitarget state estimation in an uncertain ocean environment, we address the impact of the uncertain propagation, which is ignored by the previously researches. It is demonstrated that the propagation uncertainty and target tracking results play important roles in the FIM and PCRLB. Moreover, the general framework for integrated target tracking and sensor placement is also proposed.
DEMO

Large-scale emergencies, both natural and man-made, are increasingly incurring devastating losses in terms of infrastructure and human lives. Rural areas are particularly vulnerable to such losses. Emergency preparedness and response services, in rural areas, severely lag behind that of their urban counterparts. One of the key limiting factors is the lack of adequate broadband connectivity, which limits agencies' capabilities to (i) disseminate emergency preparedness and response information to residents and (ii) efficiently coordinate in the face of a disaster. In this demo, we present the EApp; a smartphone application that strives to improve the information access regarding emergencies for rural residents and first responders.
Multi-expression Generative Adversarial Networks for Facial Expression Synthesis

Facial expression synthesis has always been a research hotspot in the field of computer vision and graphics. The facial expressions are complex and vary from person to person. It is a challenging task to synthesize a rich and diverse facial expression. In this paper, we propose a novel network framework: Multi-Expression Generative Adversarial Network (MEGAN). We combine automatic encoders with generative adversarial networks, and innovatively integrate labels into encoders and decoders. With just a set of networks and a discriminator, we can synthesize faces with many different expressions in an orderly fashion. In addition, we use the encoder to find a low-dimensional representation of the facial image in the potential space, and to transfer the expression under the condition that most facial features are preserved. We don't need pairs of face images, and huge amounts of data. The results of the later experiments can show that the framework we propose is able to achieve better quality and more realistic results in facial expression synthesis.
A Technique to Pre-trained Neural Network Language Model Customization to Software Development Domain

According to the CHAOS report from Standish Group during 1992–2017, the degree of success of projects in the development of software intensive systems (Software Intensive Systems, SIS) has changed insignificantly, remaining at the level of 50% inconsistency with the initial requirements (finance, time and functionality) for medium-sized projects. The annual financial losses in the world due to the total failures are of the order of hundreds of billion dollars. The majority of information about software projects has textual representation. Analysis of this information is vital for project status understanding, revealing problems on the early stage. Nowadays the majority of tasks in NLP field are solved by means of neural network language models. These models already have shown state-of-the-art results in classification, translation, named entity recognition, and so on. Pre-trained models are accessible in the internet, but the real life problem domain could differ from the origin domain where the network was learned. In this paper an approach to vocabulary expansion for neural network language model by means of hierarchical clustering is presented. This technique allows one to adopt pre-trained language model to a different domain.
Feedforward-assisted coherent-state comparison amplifier

Quantum optical coherent-state comparison amplifiers are nondeterministic devices which can amplify rather noiselessly a weak coherent state with an unknown phase from a finite discrete set. The high fidelity and high success probability of this amplifier are accomplished by a comparison stage and some photon subtraction stages. Due to the heralding nature of these amplifiers, they work part of the time. Here, we propose a feedforward protocol to increase the heralded times by correcting the incorrect guess. Hence, we enhance the fidelity and success probability more. Finally, the schematic experimental configuration of the feedforward-assisted coherent-state comparison amplifier is proposed.
Motion Planning for Marine Vehicles with Rigid-Body Dynamics: A Balance for Economic Performance and Desired Trajectories

The paper studies the motion planning problem for marine vehicles in which both the power consumption of vehicles and the guiding effects of the desired trajectory are taken into account. An energy-saving cost with physical meaning is developed to pursue more economical maneuvering. And a tracking cost related with desired collision-free trajectories is designed to enable marine vehicles to pass through the areas we are interested in. By choosing the two costs as optimization objectives, we construct an optimization problem which obey the practical constraints, such as dynamics of marine vehicles, limitations of actuators, and the conditions of obstacle avoidance, terminal sates and arrival time. In addition, the procedure for the proposed motion planning scheme is organized into an algorithm. Finally, through comparative simulations, the effectiveness of the designed planning algorithm is verified and the tradeoff between economic and guiding performance is demonstrated.
Characterization of the discrepancies between scores and texts of movie reviews

Review websites have changed the business world, influencing the sale and purchase of products, music and movies. They allow users to evaluate a particular subject from a score and a text. Since the reviews are based on personal opinions, it is very common to find large score variations for the same movie. A reasonable score for one critic can be considered low for another. In addition, it is also possible to find variations in the texts. Some critics use negative words to describe a movie and choose a high score. While, other critics praise and choose a lower rating, compared to the other critic who used only negative words. In this work, we formulate a sentiment analysis task to analyze and quantify such discrepancies. To do that, we applied a state of the art deep learning architecture on a large collection of movie reviews posted on Metacritic. Our results reveal that the score and text are usually not compatible. Finally, we show that, while some users consistently give discrepant reviews, some movies are more likely to receive reviews of this nature.
Ancient Books of Siberia (Based on Archaeological Expeditions of the State Public Scientific &amp; Technical Library of the Siberian Branch of the Russian Academy of Sciences in 2014–2017)

The paper describes the recent findings of the search and in-office archaeographic work of the department of rare books and manuscripts of Siberia's largest library with the focus on preserving the book cultural heritage of Ancient Russia in the east of the country, including with modern technologies of copying and IT.
Knowledge-Based Adaptative Hypermedia with HAries

The theory developed for the construction of an adaptive hypermedia is presented, which has the ability to make decisions and be adjusted to the user’s needs. It is considered that besides the elements conforming any Hypermedia (nodes, links, multimedia, etc.), the specialized knowledge on how to handle information is included and too a working memory for each user which allows remember all the interaction that this one executes with the system. This type of knowledge is included in the knowledge representation scheme of hybrid language HAries, by means of three structures: Hypermedia HAries, Execution Variable and State Variable of the Hypermedia which were created for such purpose. The first stores all the hypermedia information related to the data and the knowledge of themselves in a database. This information is then used during the execution of the hypermedia to decide what to show to the user to see in every single moment. The last two structures have the function to start the execution of the hypermedia and the navigation control of the user through itself. All of them are then stored in a knowledge base created with the HAries language.
Saak-Transform based Method for Image Noise Reduction

This research proposes a novel approach to reduce noise in an image. In this study, the input image is considered being shot with high ISO in the low light condition and the noise is modeled as the additive white Gaussian noise. The Subspace Approximation with Augmented Kernels (Saak) transform, a state-of-the-art spatial-spectral representation, is used for extracting the local characteristics of an image. The clean coefficients are estimated based on optimal linear minimum mean square error (LMMSE) estimation with a shrinkage on Saak coefficients. The processed image provides subjectively satisfactory quality improvement and an increase in peak signal to noise ratio (PSNR) without harming edges or other image details. It shows that Saak transform is a promising tool for noise reduction.
Semiparametric Subsampling and Data Condensation for Large-Scale Data Analytics

Subsampling is often used to reduce the complexity of large datasets. However, such methods need to ensure that the subsampled data are representative of the original dataset. Here, we introduce a new clustering-based data condensation (subsampling) framework for large datasets. The framework relies on the use of stratified sampling, Voronoi diagrams, and variational Bayes-based Gaussian mixture clustering. We tested the proposed framework on three large imbalanced benchmark datasets, namely cod-RNA, ds1.10, and ds1.100. The efficiency and generality of the proposed framework were assessed by comparing the predictive performance of the reduced datasets with the original datasets over two machine-learning classifiers, namely the random forest, and the radial basis function network. The evaluation metrics included the accuracy, F-measure and reduction percentage. We found that very high reduction percentages can be achieved using our new framework while maintaining satisfactory predictive performance.
A New Object Detection Method Based on T Mutation PSO for SVM Parameter Optimization

In order to detect objects efficiently and accurately, we propose a high real-time and robust performance object detection method in this paper. Firstly, a feature called Locally Assembled Binary Haar-like (LABH) is selected as the feature vector and SVM is used for classifier. In addition, a new type of PSO called T mutation PSO (T-PSO) is used to select suitable parameters of SVM, which could improve the classification ability of SVM prominently. T-PSO is a novel optimization algorithm, which not only has strong global search capability but also helps to find the optimum quickly by using T mutation. The experiment carried out on videos from INRIA dataset, MIT dataset and Daimler dataset illustrates that the proposed method is real-time and feasible enough for the task of object detection.
A New Digital Signal Processing Based Model With Multi-Aspect Term Frequency for Information Retrieval

In recent decades, the vast majority of researchers in the field of information retrieval (IR) have been studying three main categories of IR models (i.e., vector space models, probabilistic models and statistical language models). Recently, some researchers have been exploring a new category of IR models which introduce the knowledge from the field of digital signal processing (DSP), which have been shown to be promising. However, the existing DSP-based models are not well-performed in some cases because they have not incorporated effective term weighting methods and the existing framework itself has some disadvantages to be overcome. In our research, we propose a new DSP-based IR model, denoted as DSP-MATF, which incorporates a very effective term weighing method from the well-performed Vector Space Model named Multi-Aspect Term Frequency (MATF). In addition, for improving the existing DSP-based IR frameworks, we consider each query term as a spectrum enveloped by different curves of seven kernel functions. To testify the effectiveness of our proposed model, we conduct extensive experiments on seven standard TREC datasets. The results show that in most cases our proposed model outperforms the strong baselines in terms of varied metrics.
Development of Data Utilization Platform for Utilizing a Large Amount of Various Data of Business Systems in the Railway Field

Recently in fields of railway and industry, it becomes more necessary to use data effectively in order to store and succeed to fields' know-how by IT and in order to plan for further increase in business efficiency. However, there is a large amount of various data from plural different railway business systems, it is difficult to utilize these data transversely. In order to solve the above problems, firstly for the railway field, we propose the data utilization platform, especially including platform architecture, data relation generating / visualizing with data relation network model and analysis component that can be reused in plural applications.
Unsupervised Standard Plane Synthesis in Population Cine MRI via Cycle-Consistent Adversarial Networks

In clinical studies or population imaging settings, cardiac magnetic resonance (CMR) images may suffer from artifacts due to variability in the breath-hold position adopted by the patient during the scan. Consistent orientation of image planes with respect to the cardiac ventricles in CMR sequences forms a crucial step in the assessment of cardiac function via parameters such as the Ejection Fraction (EF) and Cardiac Output (CO) of both ventricles, which are the most immediate indicators of normal/abnormal cardiac function. In this paper, we present a novel unsupervised approach for the realistic transformation of acquired CMR images to a standard orientation using Cycle-Consistent Adversarial Networks (Cycle-GANs). We tackle this challenge by splitting the problem into two principal subtasks. First, we consider a bidirectional generator mapping between the re-oriented image and the original, hence allowing direct comparison to the input image without the need to resort to paired training data. Second, we devise a novel loss function incorporating intensity and orientation terms, and aims to produce images of high perceptual quality. Extensive experiments conducted on the CMR images in the UK Biobank dataset demonstrate that the images rendered by our model can improve the accuracy of the image derived cardiac parameters.
Cleaning Data Sets with Diagnostic Errors in the High-Dimensional Feature Spaces

The paper proposes a new approach in data censoring, which allows correcting diagnostic errors in the data sets in case when these samples are described in high-dimensional feature spaces. Considering this case as a separate task is explained by the fact that in high-dimensional spaces most of the methods of outliers detection and data filtering, both statistical and metric, stop working. At the same time, for the tasks of medical diagnostics, given the complexity of the objects and phenomena studied, a large number of descriptive characteristics are the norm rather than the exception. To solve this problem, an approach that focuses on local similarity between objects belonging to the same class and uses the function of rival similarity (FRiS function) as a measure of similarity has been proposed. In this approach for efficient data cleaning from misclassified objects, the most informative and relevant low-dimensional feature subspace is selected, in which the separability of classes after their correction will be maximal. Class separability here means the similarity of objects of one class to each other and their dissimilarity to objects of another class. Cleaning data from class errors can consist both in their correction and removing the objects-outliers from the data set. The described method was implemented as a FRiS-LCFS algorithm (FRiS Local Censoring with Feature Selection) and tested on model and real biomedical problems, including the problem of diagnosing prostate cancer based on DNA microarray analysis. The developed algorithm showed its competitiveness in comparison with the standard methods for filtering data in high-dimensional spaces.
Advanced Knowledge Representation and Reasoning Making Use of an Advanced N-ary Model

We discuss in this paper some aspects of NKRL, the Narrative Knowledge Representation Language. This is a high-level n-ary conceptual tool specially conceived for the representation and management of real world, dynamically characterized entities like situations, events and complex events, actions (e.g., in a robotics context) scripts/scenarios/narratives etc. After having pointed out some shortcomings of the standard ontological solutions for dealing with this sort of information, and having recalled some general characteristics of NKRL (like the addition of an "ontology of events" to the usual "ontology of objects"), we focus on the rules/inferential aspects proper to this language. We introduce, then, the general, formal model of "rule" used in an NKRL context and we show how this can be appropriately adapted to the setup of advanced types of inference operations based, e.g., on "analogical" and "causal" reasoning.
Two-Semester Physics Course for Bachelors at Technical University

The article discusses two major problems in teaching physics for students majoring in IT at a technical university: 1) the forced change in the course structure and content due to the reduction in the time allotted for studying physics from three to two semesters, and 2) the uneven educational background of applicants, due to the fact that when entering a university, the results of the Unified State Examination in physics are not taken into account. The article analyzes the experience of the Physics Training Center at the ITMO University in St. Petersburg on conducting additional consultations on the school curriculum materials and on the development and using of electronic educational materials. The relevance of courses that are concentric in their structure, created by colleagues from the other universities, is noted. The authors propose to organize training for first-year students in various educational tracks, depending on their starting level. The article emphasizes the importance of maintaining completeness and ideological value of the course when revising the structure and content. The following conceptual scheme of the course is proposed: elementary physics as axiomatic science, statistical physics, quantum physics, quantum field theory (general concepts). The experience of the development and using of electronic educational materials (lecture presentations, electronic summaries of lectures and exercises) at ITMO University is analyzed. Despite the fact that, in general, e-learning elements make it possible to compensate the lack of class time caused by the program changes, students learn e-courses at different levels. It is noted that the reason, apparently, lies in the psychological unreadiness of a number of students to switch to “blended learning”. It is emphasized that in the mixed course creation the development of materials for assessing student performance and their final certification is a fundamentally new and time-consuming task, the preference is given to the traditional full-time exam. The authors consider that it expedient to preserve the traditional structure of the physics course at technical university.
Towards Configurable Vulnerability Assessment in the Cloud

Security is considered as one of the top embedding factor for migrating SME services and applications in the cloud. However, various technological advancements in cloud security actually raise the security level with respect to traditional, on-premise deployment models. Nevertheless, SMEs, while being aware of these advancements, do not apply them as early as possible during the design of their cloud products and services. In other words, they neglect the main benefits that security-by-design offers. Further, SMEs actually employ one or more open-source security tools without properly configuring them to fit the current context. This creates three main issues: (a) a waste of resources can occur; (b) the protection level achieved can be unsuitable; (c) improper accuracy in vulnerability and security event detection could lead to taking wrong actions or to not reacting in critical security events. This paper proposes a security-by-design solution which focuses on vulnerability assessment and attempts to deal with the first and third from the aforementioned issues. These issues are addressed through: (a) the supply of a configuration meta-model enabling to properly configure the vulnerability assessment to have the right accuracy and performance level without impacting the precious resources available for the proper functioning of the SME's applications; (b) the orchestration of various kinds of vulnerability scanning tools which enable increasing the scanning accuracy.
Path Planning for Mobile Robot Based on Cubic Bézier Curve and Adaptive Particle Swarm Optimization (A2PSO)

In this work, a new approach is proposed for getting a solution of path-planning for mobile robot based on cubic Bézier curve and adaptive particle swarm optimization (A2PSO). Paths generated using a cubic Bézier curve are optimized globally through the A2PSO algorithm. The A2PSO algorithm is significantly more powerful than conventional PSO algorithm. Our approach was successful in determining the shortest path in several environments full of obstacles compared to the performance of the conventional PSO.