Reviewerly/exHarmony huggingface dataset:
Biased Mixtures of Experts: Enabling Computer Vision Inference Under Data Transfer Limitations

We propose a novel mixture-of-experts class to optimize computer vision models in accordance with data transfer limitations at test time. Our approach postulates that the minimum acceptable amount of data allowing for highly-accurate results can vary for different input space partitions. Therefore, we consider mixtures where experts require different amounts of data, and train a sparse gating function to divide the input space for each expert. By appropriate hyperparameter selection, our approach is able to bias mixtures of experts towards selecting specific experts over others. In this way, we show that the data transfer optimization between visual sensing and processing can be solved as a convex optimization problem. To demonstrate the relation between data availability and performance, we evaluate biased mixtures on a range of mainstream computer vision problems, namely: (i) single shot detection, (ii) image super resolution, and (iii) realtime video action classification. For all cases, and when experts constitute modified baselines to meet different limits on allowed data utility, biased mixtures significantly outperform previous work optimized to meet the same constraints on available data.
Stratagems: Embedding Cognitive Training in Game-Based Environments

Combat Rescue Helicopter aircrew are tasked with challenging missions, usually under conditions of time pressure, dynamic conditions, and a high degree of uncertainty. The complex cognitive skills required to be effective are primarily acquired and maintained through on-the-job experiences. One strategy for supporting cognitive skill-acquisition is to develop scenario-based training that increases exposure to challenging missions in a safe environment. This paper describes a research project to develop a pedagogically effective game-based trainer for cognitive skills required for Combat Rescue Helicopter aircrew. Training was developed using a decision-centered framework to promote cognitive authenticity. The Stratagems trainer poses critical decisions and provide expert feedback utilizing ShadowBox®, a coaching technique that enables trainees to obtain insight into the decision-making processes and reasoning of experts.
A Reference Model for a Service Level Agreement

Information sharing between government organizations is regulated by Service Level Agreements (SLA’s). Design and implementation of an SLA demands involvement representatives of several organizations. They need to communicate with the same concepts and validate the requirements for the service and quality indicators. In order to support the design of an SLA and its monitoring, we propose related concept, goal and protocol reference models. The first conceptual model view is built using a literature review. The next model views include the details found by analysis of existing SLA’s. The novelty of our models is that they compose an SLA from service level objectives (SLO’s), explain the meaning of SLO’s monitoring, support execution of an SLA and expose the monitoring logics.
Character Detection in Animated Movies Using Multi-Style Adaptation and Visual Attention

Automatic identification of fictional characters is one of the primary analysis techniques for video content. A common approach to detect characters in live-action movies involves detecting human faces; however, this approach cannot be used in non-realistic domains, such as animated movies. Detection of characters in animated movies presents two major challenges: the same subject of character can be expressed in various unique styles, and there are no stylistic or other restrictions on the nature and design of character objects. To address these challenges, we introduce the “animation adaptive region-based convolutional neural network” model to detect characters in animated movies and determine whether the detected characters are human or non-human types. Our model extends the Faster R-CNN model, which is a two-stage object detector, in the following manner: 1) we add a hierarchical animation adaptation module to learn the variety of unique styles from animated movies using a single model; 2) we incorporate a double-detector architecture to focus on the regions that are visually important in determining the character class. We build a new dataset for the animated character detection task. Experiments on this dataset show that our model outperforms other existing representative object detector models in terms of character detection. Furthermore, our model achieves significant performance improvements compared with previous state-of-the-art methods used for the character dictionary generation task. Our model is robust for a variety of animation styles and can find common visual representations of all types of characters, providing an effective way to detect animated characters.
Exploring Public Playgrounds through A Data-Enabled Design Approach

A shared goal set by many local governments is to stimulate physical activity in neighborhoods. Public playgrounds play an important role in governmental policies for promoting physical activity. Although these playgrounds are generally considered beneficial for participation in physical activity, detailed data on their use is lacking. As a result, it is not clear to policymakers whether their policy choices are the right ones and designers cannot sufficiently align their design choices with the actual behavior of their end users. This Work-in-Progress presents a sensor-based data collection approach to collect detailed data in a real-life setting over a longer period of time. With this, we adapted the Data-Enabled Design process towards public environments by combining a quantitative sensor implementation alongside qualitative research. We show findings from two months of data collection on seven playgrounds and discuss next steps in the Data-Enabled Design framework.
INFLUENCE OF LIBRARY ACTIVITIES ON STUDENTS’ READING DYNAMISM

The article presents the results of research carried from 1997 up to 2018 on the basis of the scientific library of the Omsk State Technical University. A definite part of the research was done together with the Department of Library and Information Work of Dostoevsky Omsk State University: the comparative analysis of the meaning aspects of students reading (types and kinds of literature, genres and authors); reading dynamism; factors and motivations influencing the readers’ activity; regularity of visiting the library. Special role is devoted to correlation of reading books in electronic and traditional forms. Changes in authors’ preferences that depended on many factors (environment, information technologies, library activities, advertisement offers, etc.) are examined in detail due to the period under study. Social networks as a new factor influencing the junior reading are observed. An important place in the article is given to the practical activity of the scientific library of the Omsk State Technical University for to promote reading among students. The article presents an analysis of educational and leisure activities of the library, in which literary and artistic, creative, intellectual competitions and projects, library actions for students take place. Competition for the best reading group, literary artistic projects “Word modulations” and “Tints of colors”, students’ interuniversity literature society “Poetic studio” activities and others play an important role. The results of practical researches are visualized with the help of figures (diagrams, graphs) illustrating research data. This makes it possible to identify the factors of the library activity influence on reading dynamism in the period from 1997 up to 2018, correlate common and particular in artistic preferences, youth’ scientific interests and also aims of their visiting the library.
Research and Development of Fine Management System for Underground Trackless Vehicles

This paper researches and designs a fine management system for underground trackless vehicles. The system mainly consists of vehicle base station, location tag, wireless communication base station, and fine management platform software. And it designs algorithms and identification logic of vehicle operation process. We tested the system in a mine, and the results show that it can realize the function of location recognition of underground vehicles, trajectory recording and analysis, screening abnormal phenomena (unloading disorder) and alarm.
Generation of Plausible Incident Stories by Using Recurrent Neural Networks

Recent artificial intelligence (AI) technology is capable to generate new texts (so that it makes new stories) by learning order of words in particular datasets. This paper reports an experiment of generation of imaginal incident reports by AI text generator, which learn from real aviation incident reports. AI tries to synthesize new texts to maintain similarity of word order pattern to training data of real reports, while it may generate brand-new stories due to randomness in its algorithm. We may find hidden risk of human error incidents among those imaginal reports. Although theoretical capability of AI text generation is widely approved, it is still a practically hard problem, which requires large costs and massive amount of data. Therefore, the main contribution of this paper would be not only qualitative evaluation of made imaginal reports, but also quantitative evaluation of its feasibility.
SciBabel: a system for crowd-sourced validation of automatic translations of scientific texts

Scientific research is mostly published in English, regardless of the researcher's nationality. However, this growing practice impairs or hinders the comprehension of professionals who depend on the results of these studies to provide adequate care for their patients. We suggest that machine translation (MT) can be used as a way of providing useful translation for biomedical articles, even though the translation itself may not be fluent. To tackle possible mistranslation that can harm a patient, we resort to crowd-sourced validation of translations. We developed a prototype of MT validation and edition, where users can vote for that translation as valid, or suggest modifications (i.e., post-editing the MT). A glossary match system is also included, aiming at terminology consistency.
Research on Visual Relation Detection Based on Computer Vision

Extracting semantic information from unstructured data, such as images or text, is a critical challenge in artificial intelligence and a long-lasting research direction. In general, semantic information is captured in more meaningful ways by the relationship between objects. In particular, visual relationship can be represented by triples of this form (subject, predicate, object). Visual relationship detection needs to identify objects in the image and their location, as well as identify relationships between objects. In this paper, we review the methods about visual relation detection, analyses the algorithms at home and abroad, and compare the advanced and inadequacies between algorithms. Finally, we look forward to the development trend of visual relation detection.
Cluster Based Analysis for Google YouTube Videos Viewers

In the modern scenario, of the large amount of internet users and everyone are friendly with Google YouTube apps. Online videos are especially interesting as a potential vector for social correspondence. Video is additionally ready to catch social encounters. YouTube is the place where sounds and videos have been seen by the clients. Every person needs to watch/views their fascinating sound and recording materials they visit YouTube is where there is no bar age or sexual orientation. Everyone inquiries they vary own decision. YouTube have lots of videos collections of different types. In these regard, a number of viewers have seen different channels and videos. YouTube has a great part of the client watching their most loving channels and videos. In the previous research paper title "Data Mining Techniques for Videos Subscribers Google YouTube" taken data mining classifiers and find out the results. Now in this research paper analysis YouTube videos views and analysis with clustering and achieve a cluster quality.
Research on the Application Trend of Immersive Game Interactive Experience Space in Museum

In view of technology application trends in immersive game interactive experience, this study starts from human-medium interactive language and sums up museum space creation methods, creation ideas and development trends in immersive game experience design. To enhance the audience’s perception in museum experience, designers and participants are guided to a new perspective of the interactive language and form of presentation of museum technology application integrating with modern art. The study is divided into three discussion sections. The first section expounds on the impact of technology development trends in immersive game interactive experience on design, including the change in design methods. The second section points out that the introduction of immersive game interaction technology provides more creation guidance for information presentation of museum design language and space theme. The third section, through the analysis of the latest development trend in the technology language of immersive game interactive experience as a unique medium, concludes how museum information utilizes this latest technology for spatial experience and information dissemination as well as what role and function immersive game interactive experience technology performs in such information dissemination.
Design and Development of Easy Access Crisper/Shelf in a Refrigerator

The category of premium refrigerators available in market is different from regular household refrigerators, with the former being bigger in size. In such refrigerators, the depth can go up to 70–80 cm, and the items placed at the back side or in corners are ergonomically less accessible. The taller items need to be placed at the back side of the refrigerator to easily access the short items. Also, due to the limitations of the support structure, the full extension of the crisper is not achievable. Currently, the crisper is pulled out only in a linear manner. Hence, it is advantageous to make the crisper rotate to make it accessible from more number of sides. In this paper, a mechanism to achieve the rotational motion of the crisper was developed. Finite element analysis (FEA) was carried out to check the permissible deflection of the crisper and is validated using experimentation.
Automatic 3D Modelling for Heel Based on the Images of 2D Sketches

Shoe development and manufacturing are enhanced by creating 3D models of shoe parts. As an independent and important part of shoe, the 3D model of heel is often generated manually based on 2D design sketches. To improve the modelling efficiency, this paper proposes a sketch-image-based automatic 3D modelling method for heel. The images of 2D design sketches are analyzed to determine coordinate information, which is converted into geometric coordinate values usable to create a 3D model of heel. The modelling method uses 3D geometric rules derived from the analysis of strategies adopted by skilled manual operators. It leads to a new process that links the traditional approach with advanced technologies. A case study shows that this method can build the heel model automatically.
Criminal Process Initiated Electronically through Police Web Portals: A Comparative Research

This study deals with the mechanism for initiating the criminal process provided by public administrations through the electronic use of legal instruments in police web portals. These ICT systems allow citizens to be brought closer to government related to justice action, strengthening of C2G relationship reflected in the protection of collective citizen security. For this, 29 electronic platforms of police agencies for online reporting are studied and their substantial legal and technological characteristics are revealed.
An Assessment of Substitute Words in the Context of Academic Writing Proposed by Pre-trained and Specific Word Embedding Models

Researchers who are non-native speakers of English always face some problems when composing scientific articles in this language. Most of the time, it is due to lack of vocabulary or knowledge of alternate ways of expression. In this paper, we suggest to use word embeddings to look for substitute words used for academic writing in a specific domain. Word embeddings may not only contain semantically similar words but also other words with similar word vectors, that could be better expressions. A word embedding model trained on a collection of academic articles in a specific domain might suggest similar expressions that comply to that writing style and are suited to that domain. Our experiment results show that a word embedding model trained on the NLP domain is able to propose possible substitutes that could be used to replace the target words in a certain context.
DeepConAD: Deep and Confidence Prediction for Unsupervised Anomaly Detection in Time Series

The current digital era of Industrial IoT and Automotive Technologies have made it standard for a large number of sensors to be installed on machines or vehicle, capture and exploit time-series data from such sensors for health monitoring tasks such as anomaly detection, fault detection, as well as prognostics. Anomalies or outliers are unexpected observations which deviate significantly from the expected observations and typically correspond to critical events. Current literature demonstrates good performance of Autoencoder for anomaly and novelty detection problems due to its efficient data encoding in an unsupervised manner. Despite the unsupervised nature of autoencoder-based anomaly detection methods, they are limited by the identification of anomalies using a threshold that is defined based on the distribution of reconstruction cost. Often, it is difficult to set a precise threshold when the distribution of reconstruction cost is not known. Motivated by this, we proposed a new unsupervised anomaly detection method (DeepConAD) that combined Autoencoder with forecasting model and used uncertainty estimates or confidence interval from forecasting model to identify anomalies in multivariate time series. We performed an experimental evaluation and comparison of DeepConAD with two other anomaly detection methods using Yahoo benchmark dataset, which contain both real and synthetic time series. Experimental results show DeepConAD outperforms other anomaly detection methods in most of the cases.
Analysis of Reading Offers: The Case of Finland and Germany

Submitting offers is part of the daily routine in sales and one important question is what gets read of such offers and where is attention oriented. We used eye tracking for testing a one-page quotation on a technical product. It seems that Germans and Finns have a similar pattern of attention and fixated on all relevant aspects of the offer, while Germans did not pay as much attention to pricing details and terms and conditions, and did not fixate as often as the Finnish participants. Finnish participants took more time reading the examples and had slightly more difficulty understanding the information.
Secure Retrieval in Outsourced Environment using Genetic Algorithm

Cloud Computing is getting prevalent and as development is speeding up, more and more sensitive information is being centralized onto the cloud, such as e-mails, personal health records, company finance data, and government documents etc. The data owners and cloud server are not in the same trusted domain for the data outsourcing. This may put the outsourced unencrypted data at risk, leading to leakage of sensitive information to unauthorized entities or even it can be hacked by the cloud server. To avoid leakage and to achieve security and maximize the usability, we propose the advance techniques of both Cryptography and Information Retrieval communities, in the spirit of "as-strong-as-possible" security guarantee. The paper focuses on online secure information retrieval using genetic algorithms. It increases information retrieval relevancy. Vector Space Model is used for information retrieval based on the similarity measurement between query and documents. Documents with high similarity to query are judge more relevant to the query and should be retrieved first. Also, main aim of this paper is to increase the relevancy over exact match in secure encrypted domain. The process aimed at finding the effective and best searching technique over cloud data with the enhanced security.
Human action recognition with deep learning techniques

We aim to provide an approach for human action recognition, based on combination of transfer knowledge from pre-trained deep network architectures and knowledge from another modality using the same deep network without pre-trained weights. Upon fine tuning networks that have been trained within a similar context, we evaluate our approach on a dataset that is formed by medical conditions and demonstrate the potential of our approach in challenging cross-subject and cross-setup real-like scenarios.
Web-based Tool for Educational Environment and its Providing Services to Design, Report and Analyze Questionnaire/Testing

Questionnaire and online testing are very important for teachers and students to realize their effective education as well as to improve their environment. This paper describes a Web-based tool to support educational environment, which can provide suitable services to design easily, to carry out smoothly, to report visually and to analyze statistically questionnaire/testing. We have developed our Web-based tool with “Ruby on Rails”, applied it to real lectures and evaluated it based on feedbacks from teachers and students through practical educational situations. With such an application, teachers will be able to carry out Web-based questionnaire/online testing, to review or score the feedback from students and to visualize students' understanding levels easily and smoothly.
A Survey on Algorithms in Deep Learning

In the last couple of years, artificial neural networks have gained considerable momentum. Their results could be enhanced if the number of layers could be made deeper. Of late, a lot of data has been generated, which has led to big data. This comes along with many challenges like quality, which is one of the most important ones. Deep learning models can improve the quality of data. In this chapter, an attempt has been made to review deep supervised and deep unsupervised learning algorithms and the various activation functions used. Challenges in deep learning have also been discussed.