Reviewerly/exHarmony huggingface dataset:
Development of Professional Creativity of Teachers in the System of Professional Safety Culture of Children in Transport

Relevance of the research. Transformation of fundamental bases of education actualized the problem of teachers’ professional creativity development, ensuring the application of innovative approaches in the system of qualification for the formation of a culture of safety in transport. The analysis revealed a contradiction between understanding the role of creativity in the system of postgraduate pedagogical education and insufficiency of this issue in theory and pedagogical practice. The purpose of the study: theoretical substantiation and practical realization of the development of professional creativity of teachers within the framework of the project of the professional development of education workers on the formation of children transport security culture.Methods of research. The research was carried out on the basis of scientific-pedagogical methods of analysis, comparative generalization and systematization of theoretical and empirical sources, questionnaires determining the necessary objectivity and heuristic conclusions.Results of the study. The work establishes the essence, indicators, conditions and levels of development of professional creativity of pedagogical staff in the system of advanced training in developing a safety culture for children based on innovative educational technologies. A developed cluster of innovative technologies implemented in the digital educational environment of the University of Minin (MOOC), which represents the novelty of the research, is proposed.Discussion and conclusion. The cluster was based on culturological, competency-based, problem-situational, environmental, and contextual principles. In the development of a creative teacher the main role is played by following groups of pedagogical technologies: technologization and goal-setting, focusing on the meaning and legitimacy of the upcoming activities, understanding the importance of new relevant competencies; management of the process of assimilation of content with demonstration of examples and mastering methods of creative activity in the digital educational environment; organization of creative independent activity with the use of electronic educational resources; reflexive technologies of evaluation of the results of training, combining quantitative and qualitative methods; technologies of pedagogical communication and support, delicate management of educational activities, tutor assistance, supervision, and moderation in group discussions.
ENAS oriented layer adaptive data scheduling strategy for resource limited hardware

Efficient Neural Architecture Search (ENAS) is an effective solution for building deep Convolutional Neural Network (CNN) models automatically. However, it is confronted with challenges when concerning deploying the searched networks on embedded platforms under limited resources. The key issue is the mismatch between the traditional data scheduling and the irregularity of layers from ENAS searched networks, which results in remarkable bandwidth pressure increment, and further leads to performance degradation and power consumption increase. In this paper, three alternative data scheduling patterns are constructed for different layers from ENAS searched networks, and a layer adaptive data scheduling strategy is proposed according to the constrained resources given by embedded platforms. Additionally, an adaptive architecture is also presented to deploy the searched networks efficiently, providing 4-10x performance speedup and 2.5-6x power consumption saving.
Fractal dimension and chromatin textural analysis to differentiate follicular carcinoma and adenoma on fine needle aspiration cytology

A new form of technology is applied to cytology specimens in an attempt to solve the problem of distinguishing follicular adenoma from follicular carcinoma of the thyroid.
Capturing Regularity of ADL Routines Using Hierarchical Clustering Models

Nearly one in four community-dwelling elders are affected by mild cognitive impairment, such as dementia. As gradual changes in daily routine is a major symptom of cognitive diseases, the longitudinal monitoring of routine uniformity in a smart home environment can greatly contribute to the early identification and tracking of progression of such diseases. However, the high level of complexity in activity patterns and large amount of noise stemming from real life behaviors pose great challenges in achieving this task. We propose a method to quantify the degree of routineness by representing the daily activities over a span of several days as an image and identifying clusters of similar activities through hierarchical bottom-up clustering. Results from this study provide a foundation towards quantifying routine patterns and bouts from the daily routine within an elderly person's life with potential significance to early detection of outcomes of clinical interest.
Privacy-Preserving IoT Cloud Data Processing Using SGX

This paper describes a privacy-aware ecosystem for the Internet of Things (IoT) in cloud environment. Using Intel Software Guard Extensions (SGX) technology, we present an IoT middleware solution suitable for deployment on untrusted platforms, with strong assumptions that the processed data are hidden from any unauthorized party, including the cloud provider. Clients communicate with our system using an application-layer security mechanism on top of HTTP. In order to validate our contribution, we ran performance tests which suggest that the security overhead of our system remains acceptable for real-life applications.
Predicting Functional Transparency and Privacy Concerns as Future Challenge for Diffusion of IOT and Blockchain

The study predicts the significant role of 'technology transparency' in terms of its functionality to define the success or failure of any ICT solution comprising the diffusion of IoT with Blockchain. The consumers of cryptocurrency (as an example of Blockchain) taken under consideration. Specifically, 371 cryptocurrency users surveyed quantitative through electronic platform. The findings conclude that the consumer's concerns about IoT's privacy related issues are still highly questionable. In the meanwhile, blockchain is perceived as secure and reliable. The outcome highlights that the diffusion of IoT and Blockchain still holds great surprises and challenges with significant potential to be explore and address.
Estimating the Spatial Resolution of Very High-Resolution Overhead Imagery

We investigate the problem of estimating the spatial resolution of overhead imagery. More overhead imagery is becoming available without such meta-data either because it was not collected in the first place or was not preserved with the imagery. Knowing the spatial resolution can be important for a range of automated image understanding tasks such as object detection, semantic segmentation, etc. In this paper, we explore a regression framework with a feature extraction frontend and a dilated convolution backend to estimate the spatial resolution of an overhead image. We show that a stacked auto-encoder frontend outperforms a standard convolution neural network feature extractor. In order to demonstrate our approach, we construct an evaluation dataset consisting of a large collection of very high-resolution overhead images with spatial resolutions ranging from 0.15 to 1.0 meters per pixel.
Emoticon and Text Sarcasm Detection in Sentiment Analysis

A lot of work has been attempted in the area of sentiment analysis (SA)/opinion mining of natural language texts (NLT) and social media. One of the major objectives of such tasks is to allocate polarity either positive (+ve) or negative (−ve) to a part of the text. But, at a similar time, the problem of assigning the degree of positivity and negativity of particular text occurs. The problem becomes more difficult in the case of text gathered from social sites, as these sites contain a number of emoticons and sarcasm words that have hidden meaning along with the expressions. In this paper, we have presented an emoticons and text sarcasm detection system. The value of the uploaded text document is generated by removing the stop words and data filtration process. Here, two types of polarities are identified, namely positive and negative for both sarcasm and emoticons with 100% accuracy. For classifying the polarities, artificial neural network (ANN) is used as a classifier. At last, the comparison between proposed work and existing work is discussed.
Dynamic Bayesian Networks in the Problem of Localizing the Narcotic Substances Distribution

This paper proposed a methodology for the use of static and dynamic Bayesian networks (BN) in the problems of localizing the distribution of narcotic substances. Methods for constructing the BN structure, their parametric training, validation, sensitivity analysis and “What-if” scenario analysis are considered. A model of dynamic Bayesian networks (DBN) for scenario analysis and prediction of the composition of a narcotic substance has been developed. The model was designed in collaboration with law enforcement officers, as well as forensic experts in the selection and quantification of input and output variables.
Chinese named entity recognition in power domain based on Bi-LSTM-CRF

Efficient recognition of proprietary entities is an important basic work for text data mining and intelligent application in power domain. Traditional power domain Named Entity Recognition (NER) methods rely on feature engineering seriously, which unable to learn power entity features automatically. In order to learn entity features automatically and extract power domain named entities efficiently, a model based on Bidirectional Long Short-Term Memory Neural Networks (Bi-LSTM) and Conditional Random Field (CRF) was proposed in this paper. Word representations were fed into the neural networks as an additional feature and Skip-gram embeddings were trained on power domain corpus. Experimental results showed the precision rate reaches higher than 88.25% and the recalling rate reaches higher than 88.04%, which confirm the method based on Bi-LSTM and CRF is effective for named entity recognition in the power domain.
Search Condition-Hiding Query Evaluation on Encrypted Databases

Private database query (PDQ) is a protocol between a client and a database server, designed for processing queries to encrypted databases.Specifically, PDQ enables a client to submit a search query and to learn a resulting set satisfying its search condition, without revealing sensitive information about a query statement.The whole query can be protected from the server, but for efficiency reasons known PDQ solutions generally consider to hide the constants only in a query statement.In this paper, we provide two fully homomorphic encryption (FHE)-based PDQ protocols that hide type of queries as well as the constants of a query statement.Particularly, our constructions focus on conjunctive, disjunctive, and threshold conjunctive queries.To this end, we first build a single compact logical expression to cover both conjunctive and disjunctive queries.On top of the logical expression, we design a PDQ protocol that enables to evaluate conjunctive and disjunctive queries without revealing any information on a given query.The second PDQ protocol comes from our observation that if a threshold conjunctive query has a particular threshold value, it results in either a conjunctive query or a disjunctive query.Because the PDQ protocol writes the three types of queries into a single polynomial expression, the resulting protocol can evaluate the three types of query statements without revealing any information on queries.To demonstrate their efficiency, we provide proof-of-concept implementation results of our proposed PDQ protocols.According to our rudimentary experiments, it takes 37.57 seconds to perform a query on 316 elements consisting of 16 attributes of 64 bits using Brakerski-Gentry-Vaikuntanathan's leveled FHE with SIMD techniques for 149-bit security, yielding an amortized rate of just 0.119 seconds per element.
A harmony‐based approach to generating sequential color schemes for maps

Abstract Generating high‐quality sequential color schemes for maps is challenging for most mapmakers. This article presents a harmony‐based approach for automatically generating sequential color schemes for maps. The proposed approach consists of two steps: (a) color harmony quantification for sequential color schemes on maps and (b) color scheme generation using quantified color harmony factors. The approach was tested using three experimental maps, including the Chinese water body density index map, Chinese vegetation cover index map, and Chinese population density map. Effectiveness was evaluated by comparing color schemes generated by the proposed approach with those created by the probability‐based method and ColorBrewer. Twenty‐six participants were invited to rate each map color scheme using a 5‐point scale. A t test was also used to examine the significance of the difference. The results show that the mean points of color schemes using the proposed approach are higher than those using the probability‐based method, and the corresponding P ‐values are far lower than .05, which suggests that the proposed approach is better than the probability‐based method and can improve the sequential color scheme quality in automatic ways. The mean points of color schemes created using the proposed approach are also slightly higher than or similar to those of ColorBrewer, which are well‐known map color schemes manually designed by an experienced cartographer. This result further suggests that the proposed approach can meet the requirement of automatic generation for high‐quality sequential color schemes on maps.
An Autonomous Task Algorithm Based on Behavior Trees for Robot

This paper proposed an autonomous task algorithm based on Behavior Trees (BTs) for robot, which enabled robot to complete tasks autonomously in dynamic environment with understanding user's intention. Different from other advanced robotic systems, we proposed a unified representation model based on BTs. It achieved the process of transformation from abstract intention to robot specific operation and had been applied on robotic component of human-robot interaction and reasoning, task planning, execution. Then, the autonomous task algorithm was designed, which generated a plan according to the user's intention, map it into a BTs model and execute it. The plan could be updated dynamically through the status feedback of execution, iteratively accomplished a series of ordered actions to complete the task. We designed simulations and actual experiments respectively to verify the feasibility and practicality of our methods. The success rate of simulation and actual experiments were both 100%.
An Explanation Method for Siamese Neural Networks

A new method for explaining the Siamese neural network is proposed. It uses the following main ideas. First, the explained feature vector is compared with the prototype of the corresponding class computed at the embedding level (the Siamese neural network output). The important features at this level are determined as features which are close to the same features of the prototype. Second, an autoencoder is trained in a special way in order to take into account the embedding level of the Siamese network, and its decoder part is used for reconstructing input data with the corresponding changes. Numerical experiments with the well-known dataset MNIST illustrate the propose method.
Two-level Q-learning: learning from conflict demonstrations

Abstract One way to address this low sample efficiency of reinforcement learning (RL) is to employ human expert demonstrations to speed up the RL process (RL from demonstration or RLfD). The research so far has focused on demonstrations from a single expert. However, little attention has been given to the case where demonstrations are collected from multiple experts, whose expertise may vary on different aspects of the task. In such scenarios, it is likely that the demonstrations will contain conflicting advice in many parts of the state space. We propose a two-level Q-learning algorithm, in which the RL agent not only learns the policy of deciding on the optimal action but also learns to select the most trustworthy expert according to the current state. Thus, our approach removes the traditional assumption that demonstrations come from one single source and are mostly conflict-free. We evaluate our technique on three different domains and the results show that the state-of-the-art RLfD baseline fails to converge or performs similarly to conventional Q-learning. In contrast, the performance level of our novel algorithm increases with more experts being involved in the learning process and the proposed approach has the capability to handle demonstration conflicts well.
Alternative Approach to Solving Computer Vision Tasks Using Graph Structures

An approach to recognizing objects on images is proposed, which uses graph structures and graph algorithms. The image being processed is converted into a grid graph, which is divided into image segments using Kruskal’s algorithm and a Gaussian blur. Each resulting segment is characterized using descriptors, which are then grouped together to form the segment’s fingerprint. In the knowledge base, which is also structured as a graph, groups of object fingerprints are linked via weighted edges, which indicate the degree of contextual association. During object recognition, neighboring segments and contextual associations are used to better predict what objects are presented in the input image.
Persona Classification of Celebrity Twitter Users

Twitter is a microblogging service allowing users to post up to 280 characters at a time describing their thoughts. Twitter currently receives about 500 million tweets a day, in which people share their comments regarding a wide range of topics. In the process of creating social network profiles, users reveal a lot about themselves in what they share and how they say it. Through self-description, status update, and tweets, we can find a lot about the users. A user's knowledge of social sites could be remarkably improved if other information like demographic attributes and user's personal interest and the interest of other users are considered. This is truer in case of celebrity users. This chapter attempts to analyze celebrity tweets to provide relevant recommendations to the practitioners. The tweets of celebrity users are classified using two distinct approaches (1) Fixed Classification into six predefined categories and (2) Generating a category if the tweet does not belong to any defined category. The first kind of classification has been done in three different ways; by individually applying Naïve Bayes, Decision Tree, and Support Vector Machine. For generating a new category, Latent Dirichlet Allocation is used. Henceforth, this Persona Classification of Celebrity Twitter Users will help users to gain insight into their interests thereby decluttering their twitter feed and showing them relevant content on their feed. With the understanding of celebrity persona, smart recommendation systems can also be designed.
Knowledge Graph Exploration: A Usability Evaluation of Query Builders for Laypeople

SPARQL enables users to access and browse knowledge graphs in a precise way. However, using SPARQL requires knowledge that many casual users lack. To counter this, specific tools have been created that enable more casual users to browse and query results. This paper evaluates and compares the most prominent techniques, QueryVOWL, SPARKLIS and the Wikidata Query Service (WQS), through a usability evaluation, using a mixed-method evaluation based on usability metrics and heuristics, containing both quantitative and qualitative data. The findings show that while WQS achieved the best results, usability problems were encountered in all tools. Key aspects for usability, extracted from the evaluation, serve as important contributions for future query builders.
Answering Naturally : Factoid to Full length Answer Generation

In recent years, the task of Question Answering over passages, also pitched as a reading comprehension, has evolved into a very active research area.A reading comprehension system extracts a span of text, comprising of named entities, dates, small phrases, etc., which serve as the answer to a given question.However, these spans of text would result in an unnatural reading experience in a conversational system.Usually, dialogue systems solve this issue by using template-based language generation.These systems, though adequate for a domain specific task, are too restrictive and predefined for a domain independent system.In order to present the user with a more conversational experience, we propose a pointer generator based full-length answer generator which can be used with most QA systems.Our system generates a full-length answer given a question and the extracted factoid/span answer without relying on the passage from where the answer was extracted.We also present a dataset of 315,000 question, factoid answer and full-length answer triples.We have evaluated our system using ROUGE-1,2,L and BLEU and achieved 74.05 BLEU score and 86.25 Rogue-L score.
Photo Filter Classification and Filter Recommendation without Much Manual Labeling

Because how users employ filters to photos may reveal user's preference or mental state, a photo filter classification method is potentially demanded to enable future large-scale analysis. We adopt the transfer learning technique to transform deep models pre-trained for object classification into models suitable for photo filter classification. Based on accurate classification results, we build a filter recommendation approach without much manual labeling. It can be easily extended when more training data are available. A series of experimental studies are conducted to demonstrate effectiveness of filter classification with transfer learning. We also demonstrate the proposed filter recommendation achieves encouraging performance.
Edge detection in noisy images with different edge types

Edge Detection in an image is a process that produces edges of image objects, the purpose of which is to mark the parts that become detailed images to improve the details of blurry images, which occur due to the effects of the image acquisition process. Edge is defined as a change in the intensity of a large distance. Based on changes in intensity, there are three types of edges in digital images, namely, step edges, ramps edge, and noisy edge. On step edges where the intensity or gray value changes very fast The Gradient Method is able to detect better. On the ramp edge where the gray value slowly changes the Laplace method is able to detect better than the Gradient Method. On a noisy edge, the existence of noise in the image can bring up the other edges around the actual edge and can also shift the actual edge position. In this study, the authors conducted edge detection in the image by means of determining the right edge detection method to detect edges in noisy images. Noise is obtained by generating a Gaussian Noise in the image. The results showed that, without filtering the image, in noisy edge, the LoG operator was able to detect edges and reduce noise better than Canny. However, by selecting the right threshold that matches the σ (standard deviation), Canny also capable to provide good edge detection results.