Reviewerly/exHarmony huggingface dataset:
Incremental Composition Process for the Construction of Component-Based Management Systems

Cyber-physical systems (CPS) are composed of software and hardware components. Many such systems (e.g., IoT based systems) are created by composing existing systems together. Some of these systems are of critical nature, e.g., emergency or disaster management systems. In general, component-based development (CBD) is a useful approach for constructing systems by composing pre-built and tested components. However, for critical systems, a development method must provide ways to verify the partial system at different stages of the construction process. In this paper, for system architectures, we propose two styles: rigid architecture and flexible architecture. A system architecture composed of independent components by coordinating exogenous connectors is in flexible architecture style category. For CBD of critical systems, we select EX-MAN from flexible architecture style category. Moreover, we define incremental composition mechanism for this model to construct critical systems from a set of system requirements. Incremental composition is defined to offer preservation of system behaviour and correctness of partial architecture at each incremental step. To evaluate our proposed approach, a case study of weather monitoring system (part of a disaster management) system was built using our EX-MAN tool.
On the Sample Complexity of Adversarial Multi-Source PAC Learning

We study the problem of learning from multiple untrusted data sources, a scenario of increasing practical relevance given the recent emergence of crowdsourcing and collaborative learning paradigms. Specifically, we analyze the situation in which a learning system obtains datasets from multiple sources, some of which might be biased or even adversarially perturbed. It is known that in the single-source case, an adversary with the power to corrupt a fixed fraction of the training data can prevent PAC-learnability, that is, even in the limit of infinitely much training data, no learning system can approach the optimal test error. In this work we show that, surprisingly, the same is not true in the multi-source setting, where the adversary can arbitrarily corrupt a fixed fraction of the data sources. Our main results are a generalization bound that provides finite-sample guarantees for this learning setting, as well as corresponding lower bounds. Besides establishing PAC-learnability our results also show that in a cooperative learning setting sharing data with other parties has provable benefits, even if some participants are malicious.
Research on document digitization processing technology

The digitalization of document information is the development direction of the digitalization of document information management, which involves various technologies such as digitalization technology, picture and text editing, storage format, etc. Through the PDF document loading display, change the page replacement storage, the technical page jump to achieve the PDF document programming control.
Adaptive interpolation based on optimization of the decision rule in a multidimensional feature space

An adaptive multidimensional signal interpolator is proposed, which selects an interpolating function at each signal point by means of the decision rule optimized in a multidimensional feature space using a decision tree. The search for the dividing boundary when splitting the decision tree vertices is carried out by a recurrence procedure that allows, in addition to the search for the boundary, selecting the best pair of interpolating functions from a predetermined set of functions of an arbitrary form. Results of computational experiments in nature multidimensional signals are presented, confirming the effectiveness of the adaptive interpolator.
PONAS: Progressive One-shot Neural Architecture Search for Very Efficient Deployment

We achieve very efficient deep learning model deployment that designs neural network architectures to fit different hardware constraints. Given a constraint, most neural architecture search (NAS) methods either sample a set of sub-networks according to a pre-trained accuracy predictor, or adopt the evolutionary algorithm to evolve specialized networks from the supernet. Both approaches are time consuming. Here our key idea for very efficient deployment is, when searching the architecture space, constructing a table that stores the validation accuracy of all candidate blocks at all layers. For a stricter hardware constraint, the architecture of a specialized network can be very efficiently determined based on this table by picking the best candidate blocks that yield the least accuracy loss. To accomplish this idea, we propose Progressive One-shot Neural Architecture Search (PONAS) that combines advantages of progressive NAS and one-shot methods. In PONAS, we propose a two-stage training scheme, including the meta training stage and the fine-tuning stage, to make the search process efficient and stable. During search, we evaluate candidate blocks in different layers and construct the accuracy table that is to be used in deployment. Comprehensive experiments verify that PONAS is extremely flexible, and is able to find architecture of a specialized network in around 10 seconds. In ImageNet classification, 75.2% top-1 accuracy can be obtained, which is comparable with the state of the arts.
Treating Cold Start in Product Search by Priors

New products in e-commerce platforms suffer from cold start, both in recommendation and search. In this study, we present experiments to deal with cold start in search by predicting priors for behavioral features in learning to rank set up. The offline results show that our technique generates priors for behavioral features which closely track posterior values. The online A/B test on 140MM queries shows that treatment with priors improves new products impressions and increased customers engagement pointing to their relevance and quality.
4th Industrial Revolution as a Challenge to the University Community

Currently, one can observe accelerated implementation of high-tech solutions including those developed on the basis of artificial intelligence in various sectors of economy and public relations, in particular, in education. In this regard, the article discusses the main challenges that the 4th industrial revolution poses to the higher education system. These challenges include the automation of a number of professions; lack of competent personnel; digitalization of the educational process, etc. The prospects of introducing a blockchain to fix personal achievements in the context of the development of artificial intelligence technologies in particular, are highlighted. The possibilities of processing large volumes of data (Big Data) are described in order to create ratings of universities, departments and teaching staff. The experience of introducing artificial intelligence technologies into the creation of an individual educational path and the formation of a «digital footprint» at the STI University «20.35» is analyzed. Namely, the creation of a digital fingerprint and its application; building a student’s optimal development path; effective team building and improving the effectiveness of e-learning are considered. The conclusion is made about the need to preserve the universal basic component of higher education.
Optimization of Data Encryption Modeling Using RSA Cryptography Algorithm As Security E-Mail Data

Abstract Along with advances in information technology, data security is a very important thing. RSA is an asymmetric cryptographic algorithm that uses a pair of keys, namely public key and private key. This research applies the RSA algorithm to solve the optimization problem in the encryption process by increasing the presentation of the encryption process more quickly and effectively with the aim of safely sent emails.
Radially Distorted Planar Motion Compatible Homographies

Fast and accurate homography estimation is essential to many computer vision applications, including scene degenerate cases and planarity detection. Such cases arise naturally in man-made environments, and failure to handle them will result in poor positioning estimates. Most modern day consumer cameras are affected by some level of radial distortion, which must be compensated for in order to get accurate estimates. This often demands calibration procedures, with specific scene requirements, and off-line processing. In this paper a novel polynomial solver for radially distorted planar motion compatible homographies is presented. The proposed algorithm is fast and numerically stable, and is proven on both synthetic and real data to work well inside a RANSAC loop. (Less)
The Role of University in Students Employment Issues

Employment of graduates from higher educational institutions depends on many factors, but primarily, on the person. The graduates’ career will depend on their competitiveness on the labor market and their ability to learn. The willingness to get a job and to be financially independent makes a lot of students start working during their studies at university. Very often their jobs are not connected with their major. The authors carried out a poll among senior students concerning their employment and the role of University in it to identify significance of their jobs and employment problems after graduation from university for them. The results of the poll are analyzed in the article, the main directions of University assistance in finding a job, in the respondents’ opinion, are specified.
Coping with opponents: multi-objective evolutionary neural networks for fighting games

Abstract Fighting games represent a challenging problem for computer-controlled characters. Therefore, they have attracted considerable research interest. This paper investigates novel multi-objective neuroevolutionary approaches for fighting games focusing on the Fighting Game AI Competition. Considering several objectives shall improve the AI’s generalization capabilities when confronted with new opponents. To this end, novel combinations of neuroevolution and multi-objective evolutionary algorithms are explored. Since the variants proposed employ the well-known R 2 indicator, we derived a novel faster algorithm for determining the exact R 2 contribution. An experimental comparison of the novel variants to existing multi-objective neuroevolutionary algorithms demonstrates clear performance benefits on the test case considered. The best performing algorithm is then used to evolve controllers for the fighting game. Comparing the results with state-of-the-art AI opponents shows very promising results; the novel bot is able to outperform several competitors.
Exploring the potential of Landsat-8 OLI and Sentinel-2 MSI data for mapping and monitoring Enez Dalyan Lagoon

This study investigated the coastal lagoon monitoring and mapping potential of Landsat-8 Opera tional Land Imager (OLI) and Sentinel-2 Multi-Spectral Instrument (MSI) in the Enez Dalyan Lagoon, located in Turkey.Five different water indices, including normalized difference water index (NDWI), modified normalized difference water index (MNDWI), automated water extraction index for shadow (AWEI sh ) and non-shadow (AWEI nsh ), tasseled cap wetness index (TCWI), and maximum likelihood classification methods were compared to evaluate the potential for mapping coastal lagoons.Additionally, spectral consistency of index algorithms was examined to introduce the coastal lagoon monitoring potential whereas cross-calibration analysis was carried out.Among the methods, the AWEI nsh achieved quite better results compared with the other methods for mapping Enez Dalyan Lagoon while TCWI had relatively poor results independent from the sensors.AWEI nsh had the strongest correlation with 0.989, while NDWI had the poorest correlation value as 0.966.Results pointed out the linear correlation and spectral consistency between almost all corresponding indices derived from the data of two sensors.This research indicated that index algorithms provide a reliable mapping of Enez Dalyan Lagoon.Also, Landsat-8 OLI and Sentinel-2 MSI sensors, which are statistically consistent, provided sufficient monitoring of Enez Dalyan Lagoon to both continuity and combined use.
Negative Aspects of the Use of Infocommunication Technologies by Undergraduate Students of Engineering Academic Programs in a Regional Russian University

A test containing 12 questions and a questionnaire comprising 15 pairs of statements have been developed.171 undergraduate students have been tested and surveyed.A statistical processing of the results has been carried out, and charts illustrating the survey results have been built.When analyzing the data, summarizing the results and formulating conclusions, a systematic approach was applied.There are six types of Internet activities that are most characteristic of the student body in question.With the help of the questionnaire based on a pair-wise comparison method, the relative time spent by the respondents on these activities was determined.The students were proved to spend approximately as much time on their studies as they do on each of the following three activities: communication with friends, hobbies and aimless surfing the network.The students are less interested in playing games or watching news.One cannot deny the benefits of using ICT for pursuing hobbies, but it is obvious the amount of time wasted on browsing the network should be reduced.Besides, it is significant to replace virtual communication with friends by their face-to-face interaction at least partially.The language of social networks is far from observing literary norms.It ignores rules of punctuation and spelling.Taken out from virtual space into real life, this youth's slang affects their behavior, way of thinking, which is, in fact, destructive for their personalities.Students' overuse of ICT is a major social problem resulting in the decline in the quality of their education.In our opinion, for engineering students the reasons for this problem lies in their insufficient level of general culture, lack of self-discipline and strong willpower, underestimated need for knowledge, and lack of a meaningful, profession-oriented goal of education.
Low Voltage Distributed Photovoltaic Power Station Connected Estimation Model Based on Dispatch Automation System

Abstract A large number of distributed photovoltaic access grids pose great challenges to the safe and stable operation of the grid. In view of the fact that the dispatching automation system does not realize the real-time acquisition and monitoring of low-voltage distributed photovoltaic data, this paper proposes the station estimation method as an auxiliary means for real-time acquisition of low-voltage distributed photovoltaic power plants, and estimates the comprehensive low-voltage distributed photovoltaic power plants through appropriate algorithms. Access. Through case analysis, the deviation between the estimated value and the measured value is basically controlled within 8%, which proves the accuracy of the estimated model. This method is of great significance for the power dispatching department to strengthen the operation monitoring and statistical management of distributed photovoltaics, and to reasonably arrange the dispatching plan and coordinate the power supply.
Machine Translation: The Case of Arabic- English Translation of News Texts

This study aims at identifying the common types of errors in Google Translate (GT) in the translation of informative news texts from Arabic to English, to measure the translation errors quality and to assess the fluency and the semantic adequacy of the translation output, and therefore to explain the extent a human translator is needed to rectify the output translation. For this purpose, some examples were purposively selected from online newspapers. The collected data was analyzed using a mixed method approach, as the errors were qualitatively identified, guided by Hsu’s (2014) classification of machine translation errors. Quantitative descriptive approach was used to measure the translation errors quality, using the Multidimensional Quality Metrics and Localization Quality Evaluation. As for assessing the semantic adequacy and fluency, a questionnaire that was adapted from Dorr, Snover, and Madnani (2011) was used. The results of the analysis show that omission, which is a lexical error and inappropriate lexical choice, which is a semantic error are the most common errors. Inappropriate lexical choice is sometimes a result of the homophonic nature of some source text words which can be misinterpreted by the machine translation system. This study concludes that it is useful to use machine translation systems to expedite the translation process, but that accuracy is sacrificed for the sake of ease (less work for the human) and speed of translation. If greater accuracy is required, or desired, a human translator must at least proofread and work on the material.
Artificially Intelligent Decentralized Autonomous Organization

Blockchains have enabled a slew of new opportunities, to create decentralized software and organizations. Riding on its tailcoats, we want to create a Decentralized Autonomous Organization (DAO) headed by Artificial Intelligence (AI). In a world where human error is majorly becoming the sole reason for mistakes, as small as a missed medication to huge ones like Chernobyl, it is becoming increasingly important to automate as many actions as possible. Creating automated organizations is only but a first step towards the goal. We decided to make an art organization which sells art to make money. The standard way of doing that would be to create a web platform like Amazon (controlled by humans) that sells art and let human artists upload their artwork. But in the interest of keeping this as automated as possible; we have decided to make a Generative Adversarial Network (GAN) which generates art. Our organization will sell this art created by the GAN and use the revenues to pay for its resources. An AI will be heading it. Hence the DAO will be an entirely human less organization capable of sustaining itself.
Revisiting the Sibling Head in Object Detector

The ``shared head for classification and localization'' (sibling head), firstly denominated in Fast RCNN~\cite{girshick2015fast}, has been leading the fashion of the object detection community in the past five years. This paper provides the observation that the spatial misalignment between the two object functions in the sibling head can considerably hurt the training process, but this misalignment can be resolved by a very simple operator called task-aware spatial disentanglement (TSD). Considering the classification and regression, TSD decouples them from the spatial dimension by generating two disentangled proposals for them, which are estimated by the shared proposal. This is inspired by the natural insight that for one instance, the features in some salient area may have rich information for classification while these around the boundary may be good at bounding box regression. Surprisingly, this simple design can boost all backbones and models on both MS COCO and Google OpenImage consistently by ~3% mAP. Further, we propose a progressive constraint to enlarge the performance margin between the disentangled and the shared proposals, and gain ~1% more mAP. We show the \algname{} breaks through the upper bound of nowadays single-model detector by a large margin (mAP 49.4 with ResNet-101, 51.2 with SENet154), and is the core model of our 1st place solution on the Google OpenImage Challenge 2019.
Functional Equivalence: An Exploration Through Shortcomings to Solutions

Abstract Since the emergence of cyberspace there have been different legal principles evolving, such as functional equivalence and technology-neutrality, with the aim to ease the regulator´s challenge of coping with the new paradigm of virtual, digital and electronic. Currently our societies have reached the doorstep of another similar disruption: infrastructures decentralized on the basis of blockchain and distributed ledger technology, or so-called cryptoeconomics. It is time to turn to cyberspace-related principles for inspiration on how to solve similar concerns, such as applying existing regulation(s) to new technological disruption. This article looks at different understandings of the functional equivalence principle, its shortcomings and the guidance it provides to regulators and courts in dealing with the challenges related to technological innovation including that of cryptoeconomics.
Advantages of Using AR (“Augmented Reality”) Technology in the Process of Teaching a Foreign Language to Non-Linguistic Students

The article is devoted to a relevant problem of introducing AR (“Augmented Reality”) technology in the process of teaching a foreign language to non-linguistic students. The researcher’s attention is focused on advantages of using this technology when forming learners’ professional foreign-language competence. The paper aims to show advantages of using AR technology when forming students’ professional foreign-language competence. The experiment results allow concluding about reasonability of introducing AR technology in the process of non-linguistic students’ foreign-language training.
Location of circular retro-reflective target based on micro-vision

In close range photogrammetry, a scale bar which has fixed targets on it is often used to scale a photogrammetric measurement by giving known distance(s) between the targets. It’s important to make sure that the scale bar can provide precise distances so the measurement can be scaled correctly. An effective way to calibrate a scale bar was realized by getting the microscopic images of the retro-reflective targets (RRT) at two ends of the scale bar and locating their centers, and the distance between two targets was measured by the laser interferometer, then the length of the scale bar could be calculated from geometric relations. In this calibration method, the problem of accurate location of RRT is a key to calibrate the photogrammetric scale bar. So in this paper, we focus on this problem and propose a RRT location scheme based on microscopic vision and image processing. The composition and principle of the target location system are introduced. According to the retro-reflective features of the RRTs and their image characteristics under the microscope, the image processing methods of preprocessing, feature extraction and centering are analyzed. The factors such as image magnification, illumination and offsets in the field of view, which may the significant sources of location error, have been tested and analyzed. The results help to standardize the RRT imaging conditions so the measurement repeatability can be improved. The repeated positioning accuracy is less than 0.3&mu;m.