Reviewerly/exHarmony huggingface dataset:
Russians' Attitude to Distance Learning: a Runet Survey

The objective of this work is to study the Russians' attitude to distance learning based on contemporary information and communication technologies. The methodological basis of the research is the use of analytical services of the Internet for studying the Russians' online activity. As an empirical basis, the work employs two Internet studies conducted by the authors in 2018 and 2019, and it analyzes the interest of the Runet [Russian network] users in the online form of knowledge acquisition. In recent years, the growth of interest in distance learning has been noted with individual seasonal drops. There is a trend to get higher education with state-recognized degrees and formally awarded qualifications. In Russia, schoolchildren of grades 10-11 extensively use online social networks to improve the efficiency of training in mathematics. They prefer Internet communities adhering to the traditional teaching principles. A combination of educational and entertaining content attracts a larger quantity of users. The results of the research are of interest for specialists dealing with the problems of distance learning and sociology of education. https://doi.org/10.26803/ijlter.18.11.22
IMAGE INPAINTING TECHNIQUE FOR HIGH QUALITY AND RESOLUTION ENHANCED IMAGE CREATION

The image in-painting is the method of improving or enhancing the damaged and the missing parts of the images. This process would be very essential preprocessing procedure in case of the medical image analysis for the diagnosis of the disease. The traditional ways of in-painting being ineffective the paper proposes hybrid image in-painting technique combining the edge connect, patch match and the deep image prior for the images to improve the quality and the resolution of the images, the proposed method is tested with different number of images from the gathered form the website to prove the competence of the proposed image in-painting technique.
Augmenting Non-Collaborative Dialog Systems with Explicit Semantic and Strategic Dialog History

We study non-collaborative dialogs, where two agents have a conflict of interest but must strategically communicate to reach an agreement (e.g., negotiation). This setting poses new challenges for modeling dialog history because the dialog's outcome relies not only on the semantic intent, but also on tactics that convey the intent. We propose to model both semantic and tactic history using finite state transducers (FSTs). Unlike RNN, FSTs can explicitly represent dialog history through all the states traversed, facilitating interpretability of dialog structure. We train FSTs on a set of strategies and tactics used in negotiation dialogs. The trained FSTs show plausible tactic structure and can be generalized to other non-collaborative domains (e.g., persuasion). We evaluate the FSTs by incorporating them in an automated negotiating system that attempts to sell products and a persuasion system that persuades people to donate to a charity. Experiments show that explicitly modeling both semantic and tactic history is an effective way to improve both dialog policy planning and generation performance.
Potential adversarial samples for white-box attacks

Deep convolutional neural networks can be highly vulnerable to small perturbations of their inputs, potentially a major issue or limitation on system robustness when using deep networks as classifiers. In this paper we propose a low-cost method to explore marginal sample data near trained classifier decision boundaries, thus identifying potential adversarial samples. By finding such adversarial samples it is possible to reduce the search space of adversarial attack algorithms while keeping a reasonable successful perturbation rate. In our developed strategy, the potential adversarial samples represent only 61% of the test data, but in fact cover more than 82% of the adversarial samples produced by iFGSM and 92% of the adversarial samples successfully perturbed by DeepFool on CIFAR10.
Implementing Big Data Processing Workflows Using Open Source Technologies

In our implementation research, we apply workflow approach to the modeling and development of the Big Data processing pipeline using open source technologies.The data processing workflow is a set of interrelated steps which launch some particular jobs such as Spark job, shell job or Postgre SQL command.All workflow steps are chained to form integrated process and imitate the data load from staging storage area to the datamart storage area.The experimental workflow-based implementation of a data processing pipeline was performed that stages through different storage areas and uses actual industrial KPI dataset of some 30 millions records.Evaluation of implementation results provides proofs of the applicability of proposed workflow to other application domains and datasets which should satisfy the data format at input stage of the workflow.
Lightweight Privacy-Preserving Equality Query in Edge Computing

As edge computing attains tremendous popularity, IoT devices always outsource their data to nearby edge servers for storing and pre-processing, which improves the efficiency of data processing and reduces the required network resources. For privacy-preserving, sensitive data is mostly encrypted before outsourcing. Nevertheless, large volumes of data in edge computing usually comes from multiple data sources, which means that they are encrypted with different secret keys, making it difficult for edge server to query and process. Existing solutions are mostly proposed for this problem in cloud computing, but they do not take into account that the limitations of computing and storage capabilities of edge devices will prevent them from performing computationally expensive operations. In this paper, we propose a lightweight privacy-preserving equality query scheme (LPEQ) in edge computing for the first time, which allows authorized users to perform equality query efficiently and privately on the encrypted data outsourced by multiple IoT devices. We also introduce a formal security model and prove that the LPEQ meets secure requirements against curious entities under this model. Meanwhile, our theoretical analyses and experimental evaluations demonstrate that the LPEQ performs better efficiency in terms of computation and communication while retaining privacy-preserving properties. Therefore, it is practical for applications in edge computing.
A logical characterization of differential privacy

Differential privacy is a formal definition of privacy ensuring that sensitive information relative to individuals cannot be inferred by querying a database. In this paper, we exploit a modeling of this framework via labeled Markov Chains (LMCs) to provide a logical characterization of differential privacy: we consider a probabilistic variant of the Hennessy-Milner logic and we define a syntactic distance on formulae in it measuring their syntactic disparities. Then, we define a trace distance on LMCs in terms of the syntactic distance between the sets of formulae satisfied by them. We prove that such distance corresponds to the level of privacy of the LMCs. Moreover, we use the distance on formulae to define a real-valued semantics for them, from which we obtain a logical characterization of weak anonymity: the level of anonymity is measured in terms of the formulae distinguishing the considered LMCs. Then, we focus on bisimulation semantics on nondeterministic probabilistic processes and we provide a logical characterization of generalized bisimulation metrics, namely those defined via the generalized Kantorovich lifting. Our characterization is based on the notion of mimicking formula of a process and the syntactic distance on formulae, where the former captures the observable behavior of the corresponding process and allows us to characterize bisimilarity. We show that the generalized bisimulation distance on processes is equal to the syntactic distance on their mimicking formulae. Moreover, we use the distance on mimicking formulae to obtain bounds on differential privacy.
AI education matters

Traditionally artificial intelligence (AI) and machine learning (ML) courses are taught at the senior and graduate level in higher-education computer science curricula following the mastery learning strategy, cf. Figure 1. This makes sense, since most AI and ML models and the theory behind them require a substantial understanding of probability and statistics, as well as advanced calculus and matrix algebra. To understand Logistic Regression as a probabilistic classifier performing maximum-likelihood or maximum-a-posteriori estimation, for example, students need to understand joint and conditional probability distributions. In order to derive the back propagation algorithm to train Neural Networks students need to understand partial derivatives and inner and outer tensor products. These are just two of many examples where substantial mathematical background - typically taught at the junior level in a computer science major program - is required. With AI and ML algorithms being used more widely by enterprises across domains, as well as, in applications and services we use in our daily lives, it makes sense to raise awareness about what AI is, what it can and cannot do, and how it is used to solve problems to a broader audience. Very much in the same spirit as the "CS for all" idea (https://www.csforall.org), we have to extend our curricula to include introductory courses to AI and ML on the early undergraduate level (or even in high-school) to expose students to the ideas and working principles of AI technology. One way to achieve this is to introduce the principles of working with data, modeling, and learning through the data science workflow.
Convolutional recurrent neural network with template based representation for complex question answering

Complex Question answering system is developed to answer different types of questions accurately. Initially the question from the natural language is transformed to an internal representation which captures the semantics and intent of the question. In the proposed work, internal representation is provided with templates instead of using synonyms or keywords. Then for each internal representation, it is mapped to relevant query against the knowledge base. In present work, the Template representation based Convolutional Recurrent Neural Network (T-CRNN) is proposed for selecting answer in Complex Question Answering (CQA) framework. Recurrent neural network is used to obtain the exact correlation between answers and questions and the semantic matching among the collection of answers. Initially, the process of learning is accomplished through Convolutional Neural Network (CNN) which represents the questions and answers separately. Then the representation with fixed length is produced for each question with the help of fully connected neural network. In order to design the semantic matching between the answers, the representation of Question Answer (QA) pair is given into the Recurrent Neural Network (RNN). Finally, for the given question, the correctly correlated answers are identified with the softmax classifier.
Reverse-Safe Data Structures for Text Indexing

We introduce the notion of reverse-safe data structures. These are data structures that prevent the reconstruction of the data they encode (i.e., they cannot be easily reversed). A data structure D is called z-reverse-safe when there exist at least z datasets with the same set of answers as the ones stored by D. The main challenge is to ensure that D stores as many answers to useful queries as possible, is constructed efficiently, and has size close to the size of the original dataset it encodes. Given a text of length n and an integer z, we propose an algorithm which constructs a z-reverse-safe data structure that has size O(n) and answers pattern matching queries of length at most d optimally, where d is maximal for any such z-reverse-safe data structure. The construction algorithm takes O(nω log d) time, where ω is the matrix multiplication exponent. We show that, despite the nω factor, our engineered implementation takes only a few minutes to finish for million-letter texts. We further show that plugging our method in data analysis applications gives insignificant or no data utility loss. Finally, we show how our technique can be extended to support applications under a realistic adversary model.
Teori Kognitif Sosial Albert Bandura (Studi Kritis Dalam Menjawab Problem Pembelajaran di Mi)

Discussion on the theory of conditioning (behaviorism) which succeeded in the field of learning during the first half of the twentieth century. Beginning in the late 1950s and early 1960s these theories were questioned in many fields. The influence of these theories is decreasing, and currently the more prominent theoretical perspectives are cognitive perspectives. One of the major challenges to behaviorism comes from observational learning studies conducted by Albert Bandura and his colleagues. The most important finding of this research is that people can learn new actions just by watching others do it. The observer does not have to carry out these actions when he learns them. Strengthening is not necessary so learning can occur. These findings refute the central assumptions of conditioning theories. Social cognitive theories that highlight the idea that most human learning takes place in a social environment. By observing others, people acquire knowledge, rules, skills, strategies, beliefs and attitudes. In learning in social cognitive MI, it is important to apply considering that children of this age do a lot of learning from observing the surrounding environment, both observing teachers, parents, and the community as a model, so it is important to create a good environment in learning MI age.Keywords: Social Cognitive Theory, Learning
MovieANN: Film Öneri Sistemlerine Çok Katmanlı Yapay Sinir Ağı Kullanarak Karma Bir Yaklaşım

The amount of data in World Wide Web is growing exponentially. Users are often lost in this vast ocean of data. In order to filter the valuable information from vast amount of data, recommendation systems are used. These systems are based on collaborative filtering, content based filtering and hybrid approaches. We combined collaborative and content-based filtering to build a hybrid movie recommendation system, MovieANN, based on neural network model. To make better recommendations in a collaborative approach, both user and movie clusters are formed. In addition to rating information, content information was also considered in the formation of the clusters. Clusters are formed according to K-Means and X-Means algorithms. Final clusters are chosen according to Davies-Bouldin Index and intra cluster distance. Homogeneity and density of the clusters are also considered. Movie and user clusters are mapped in the recommendation phase. The model is tested on a MoiveLens 1M dataset that consists of six thousand users, four thousand movies and one million ratings. Four clusters are formed to represent movie – user mappings and for each cluster, a recommendation model based on multi-layer neural network is constructed. The recommendation performance in terms of accuracy is 84.52%, 84.54% in terms of precision and 99.98% in terms of recall.
PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction

We propose an algorithm combining calibrated prediction and generalization bounds from learning theory to construct confidence sets for deep neural networks with PAC guarantees---i.e., the confidence set for a given input contains the true label with high probability. We demonstrate how our approach can be used to construct PAC confidence sets on ResNet for ImageNet, a visual object tracking model, and a dynamics model for the half-cheetah reinforcement learning problem.
Monitoring System of Graduate Employment as A Factor of Improving the Efficiency of University Educational Activities

At present, the efficiency of universities in general and the progress of graduate employment in particular is given a greater attention.To meet the criteria of graduate employment in the framework of monitoring the efficiency of higher education institutions of Russia's Ministry of Education and Science, universities create their own software systems for collecting, processing and storing information on graduate employment and modify the organizational structure to collect information on employment taking into account employment data according to the Pension Fund of the Russian Federation.Southwest State University has been developing and implementing a multi-user client-server system based on 1C: Enterprise 8.3 technology platform since 2012.There have been proposed IDEF0 functional models and the infological ER model of preliminary gathering of information about graduate potential employment and current monitoring of employment distribution channels.The optimal source of data for internal monitoring of graduate employment is the information necessarily transmitted by universities to the state information system "Federal register of educational certificates."As a result, the introduction of this system made it possible to increase the efficiency of collecting information about the current status of graduates, their employment and career development,the speed of information processing for building internal and external statistical reporting, posting information on the employment and graduates' place of work on the official website of the university.
Exploring Benefits of Transfer Learning in Neural Machine Translation

Neural machine translation is known to require large numbers of parallel training sentences, which generally prevent it from excelling on low-resource language pairs. This thesis explores the use of cross-lingual transfer learning on neural networks as a way of solving the problem with the lack of resources. We propose several transfer learning approaches to reuse a model pretrained on a high-resource language pair. We pay particular attention to the simplicity of the techniques. We study two scenarios: (a) when we reuse the high-resource model without any prior modifications to its training process and (b) when we can prepare the first-stage high-resource model for transfer learning in advance. For the former scenario, we present a proof-of-concept method by reusing a model trained by other researchers. In the latter scenario, we present a method which reaches even larger improvements in translation performance. Apart from proposed techniques, we focus on an in-depth analysis of transfer learning techniques and try to shed some light on transfer learning improvements. We show how our techniques address specific problems of low-resource languages and are suitable even in high-resource transfer learning. We evaluate the potential drawbacks and behavior by studying transfer learning in various situations, for example, under artificially damaged training corpora, or with fixed various model parts.
A study on strategy for invigorating utilization of HPC in industry based on business building blocks model

In order to invigorate utilization of high-performance computing (HPC) in industries, we have described the business building block canvas and reconsidered on HPC service model that HPC service providers would take a strategic planning to give their own abilities to small industries. And we have taken a restructuring process of the business building blocks canvas to make an innovative service strategy that HPC service organizations would adopt as their own user service model. The economic benefits of the small industries through adopting HPC services provided from the HPC service organizations effectively and efficiently have increased when the HPC service model is proportionate, targeted to the industrial present problems and introduced only when absolutely necessary. We have described two differentiated HPC service models and their achievements, and examined how effective the HPC service model of the HPC service organizations is composed to provide HPC abilities to the industry. The approach of the business building blocks canvas would help the HPC organizations to analyze and restructure the strategy.
A Declarative Method for Dynamic Multi-Agent Path Finding

The multi-agent path finding (MAPF) problem is a combinatorial search problem that aims at finding paths for multiple agents such that no two agents collide with each other. We study a dynamic variant of MAPF, called D-MAPF, which allows changes in the environment (e.g., some existing obstacles may be removed from the environment or moved to some other location, or new obstacles may be included in the environment), and/or changes in the team (e.g., some existing agents may leave and some new agents may join the team) at different times. We introduce a new method to solve D-MAPF, using answer set programming.
Designing Blockchain to Minimize Fraud in State-Owned National Insurance Company (BPJS Kesehatan)

Health insurance system in Indonesia especially BPJS Kesehatan still have problems and one of it is the possibility of fraud.Fraud in BPJS Kesehatan can be the source of loss if it not anticipated very well.Fraud can happen because of bad registration, no transparency in the data and minimum monitoring from third party outside the insurance components.This research consist of analysis and design of the fraud possibilities can be minimize and anticipated as much as possible.Blockchain is one of alternative method approach that can be applied for this matter because blockchain is good to analyze Big Data.Blockchain registered records by distributions, so that with the good records registration and distributed can minimize the possibility of the fraud.Based on the analysis result, identification and design that been made, proposed several actors, participants also activity process for each actor, business process, data requirement and transaction process that can minimize fraud in BPJS Kesehatan system.
A Survey of Aggregate Signature Applications

We survey the applications of aggregate signatures. Signatures of this type allow to aggregate different signatures produced by different users for different messages into one small signature. Given n signatures of n distinct messages, it is possible to combine them into a single signature that can be used to convince any verifier that the n users signed these n original messages. Aggregate signatures are useful for reducing the storage and bandwidth requirements and can be applied in numerous applications. In this paper, we survey these applications including PKI, blockchain, sensor networks, secure routing, fast signatures, software authentication, secure logging, and outsourced databases. For each application, we describe the types of aggregate signatures that could be used, what are the advantages and effects of using aggregate signatures there.
Automated Named Entity Recognition from Tamil Documents

Named Entity Recognition (NER) is a subsequence of words in a document that seeks to detect and classify entities into pre-defined categories such as name of the person, organization and location respectively. The impact on NER is high because a lot of Information Extraction (IE) relations are associated using Named Entities (NEs). This paper presents a pioneering method for extraction of NEs for Tamil using Supervised Learning. This hybrid framework makes use of features that are extracted based on the speciality of the Tamil language NEs. The evaluation has been done by using 1028 number of documents which comprises the standard FIRE corpus and an F-measure of 83.54% has been achieved. A performance comparison with one of the state-of-the-art Tamil NE system has been done and the proposed methodology has achieved better accuracy.