Reviewerly/exHarmony huggingface dataset:
System for Testing Physics Knowledge

The model of the physics knowledge testing system is described. The paper presents an approach to building a system for testing procedural physics knowledge, i.e. knowledge of basic physics laws and the ability to use them. This approach consists of constructing mathematical models for each academic module in a physics course. The main constructive objects are test templates, which are mathematical models of test tasks, based on physics models of systems, processes and phenomena. A template of a class of physical tests for testing knowledge of the laws of physics and the abilities of transformations of a physical system is represented by a set of elements of various formats. This set consists of geometric drawings, diagrams, graphs of functional dependencies, a system of formulas for converting physical quantities, patterns of scenarios for changing states of a physical system, and a response pattern. Each such template can be used both in generating similarity algorithms for specific multiple tests, and in algorithms for automatically checking the correctness of answers. The proposed method allows describing a relatively simple class of specific tests. An important characteristic of the system is the ability to automatically check both the final answer and the parameters of the intermediate states of the physical system. The implementation of a procedural physics knowledge testing system can be performed by creating software interactive multimedia objects using the methods of computer mathematics and algebraic programming technology. The model for the testing knowledge system in physics was implemented in the software module for the distance learning system. A model for testing the knowledge system in physics was implemented in the software module for the distance learning system.
Prototype Propagation Clustering Based on Large Margin

The idea of the prototype is important in clustering analysis. The main idea of prototype clustering is representing clusters by the compact model in the form of a set of prototypes and using the prototypes to guide the assignment of instances in the data set. Based on the idea of prototype clustering, a number of clustering algorithms have been proposed. However, most of the prototype-based algorithms focus on discovering prototypes, while the assignment step is simply handled by assigning each instance to its nearest prototype. Due to the low representability for the instances being far away from any prototypes, the nearest neighbor method may bring many mistakes. To handle this problem, in this paper, we draw large margin theory into the assignment step and propose a prototype propagation clustering algorithm. which gradually assigns the instances to the clusters. During the assignment process, the prototypes, clusters and the margin of instances are dynamically updated to guarantee sufficient representability of an instance's prototype. Visible experiments on four two-dimensional data sets are conducted to show the working mechanism of the proposed assignment method. Experimental analyses on benchmark data sets illustrate the superior clustering performance of the proposed clustering algorithm comparing with other five representative algorithms.
Bounded Swipe

Touch input has the problem that the input vocabulary is limited. In this paper, we propose the bounded swipe as a new touch gesture for solving this problem. In the bounded swipe, which extends the commonly used swipe action, the start and end points are inside a target. To test the feasibility of this gesture, we first investigated whether a bounded swipe is ever performed accidentally when a user swipes normally on a target; in 99.2% of swipes performed, the end point of the swipe was outside the target. Under the bounded swipe, the success rate was 96.7%. Therefore, the bounded swipe is a touch gesture that does not conflict with the conventional swipe.
Automatic Vehicle Beam Controller

Road accidents are a serious cause of concern and glare from headlamps of vehicles during night time create havoc for the drivers making driving really an uphill task and due to the undesirable situation, drivers meet with accidents. It's a very hectic job for the drivers to manually control the beam of the headlamps during night from time to time when an oncoming vehicle is located within 150 meters of the other vehicle. So, it's high time when a safety control unit should be installed in vehicles that can automatically dim the headlamps by detecting their intensity which, otherwise, might cause a serious trouble for the drivers. Our work proposes a cost effective and useful control unit which will automatically detect the excessive glare from headlamps and will give a signal to the oncoming vehicle causing the trouble to dim the lamps. As such, the driver's unnecessary load will be reduced and driving will become safer and smoother during nights.
Augmented Reality-Based Peephole Interaction using Real Space Information

In this paper, we investigate the effectiveness of augmented reality-based peephole interaction on a smartphone which fixes a virtual screen on a flat surface in real space and that enables a user to change the viewing area rapidly by moving or tilting the device. Since the virtual screen is superimposed in real space, the user can easily grasp the sense of distance to the virtual screen and move the device to a proper position rapidly. In addition, it is expected that the user can remember the positions of target objects easily by associating the objects with real space information. We conducted an experiment to compare the proposed augmented reality-based peephole interface with a conventional non-augmented reality-based peephole interface, and the conventional touchscreen interface. Although there was no significant difference in task completion time, the results of the questionnaire showed that the proposed interface and the conventional peephole interface were preferred to the conventional touchscreen interface and that it could be possible to show the effectiveness of the proposed interface in further investigations.
Multi-Feature Fusion for Multimodal Attentive Sentiment Analysis

Sentiment analysis has been an interesting and challenging task, researchers mostly pay attention to single-modal (image or text) emotion recognition, less attention is paid to joint analysis of multi-modal data. Most existing multi-modal sentiment analysis algorithms combined with attention mechanism focus only on local area of images, ignore the emotional information provided by the global features of the image. Motivated by the research status quo, in this paper, we proposed a novel multi-modal sentiment analysis model, which focuses on local attentive feature also on the global contextual feature from image, then a novel feature fusion mechanism is utilized to fuse features from different modal. In our proposed model, we use a convolutional neural network (CNN) to extract the region maps of images, and use the attention mechanism to acquire attention coefficient, then use a CNN with fewer hidden layers to extract the global feature, a long-short term memory model (LSTM) is utilized to extract textual feature. Finally, a tensor fusion network (TFN) is utilized to fuse all features from different modal. Extensive experiments are conducted on both weakly labeled and manually labeled datasets, and the results demonstrate the superiority of the proposed method.
Collezioni di calchi epigrafici: una nuova risorsa digitale

This paper presents E-Stampages , a new digital tool for Greek Epigraphy. It publishes online a relevant number of squeezes from the collections of the Laboratory Histoire et Sources des Mondes Antiques (HiSoMA) of Lyon, the École française d’Athènes and the Laboratory of Greek Epigraphy of Ca’ Foscari University of Venice. It stands out among similar resources for the accurate organisation of metadata, that is based upon ‘holistic’ concept of squeeze, and the 3D reconstruction. The paper describes the collaboration between the E-Stampages project, coordinated by Michèle Brunet, and the Venice Squeeze Project , coordinated by Claudia Antonetti. It highlights the shared methodologies and aims as well as the peculiarities of each of them.
Application of Streaming Media on Demand Technology in the Visual Classroom Teaching of College English

With the continuous improvement of the campus network infrastructure, the daily learning of teachers and students in schools has become prominent. Therefore, how to build a practical campus video on demand system based on the streaming media video technology has become an integral part of the campus information construction. In the transmission of the information in the visual classroom teaching of College English, the streaming media technology, which plays while downloading, can effectively change the current situation of the bandwidth limitation of the low-speed access to the Internet, overcome the shortcomings of the traditional downloading and playing methods, realize the streaming transmission of the real-time interactive video and audio resources, and enable learners to use the streaming media technology in the courses. With more freedom in choosing the learning time and place, teachers and learners can truly realize the online real-time interaction and provide learners with a learner-centered online learning environment.
Prosodic issues in language contact situations

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Wearable system based on piezoresistive sensors for monitoring bowing technique in musicians

We present an easy to wear and use wearable system to monitor wrist and elbow movements in musicians. The system is based on two piezoresistive sensors embedded into a garment. Pilot tests on an adult double-bass player were carried out to assess the feasibility of the device for monitoring bowing technique. Results show promising performances in identifying string changes and bow strokes. The proposed system allows for studying regularity and timing of bowing movements, thus being potentially useful in learning contexts, especially with beginner musicians.
Encounters

"Encounters" provides a multiparticipant audiovisual art experience with a cross reality (XR) system which consists of HoloLens units, VIVE Tracker units, SteamVR system and so on. In this experience, participants fire virtual bullets or beams at physical objects which then create a physical sound and a corresponding virtual visual effect. This is done by placing a "Kuroko" unit, which consists of a VIVE Tracker unit, a Raspberry Pi unit and a solenoid, beside a physical object using the XR system. We prepared eight Kuroko units which participants can freely place anywhere in the physical space. Then, they would interact with physical objects in that space by making sounds using the XR system. We believe that through this multiparticipant experience, participants not only experience a new form of art expression using XR but also rethink their relationship with other participants or physical objects and the environment.
Epigrafi bilingui a Roma

Greek/Latin bilingual inscriptions of Rome provide us with the opportunity to study the interaction/overlapping and linguistic mixtures between Greek and Latin speakers in the multiethnic Roman population from different points of view. By studying such texts as a whole (heterogeneous for typology, production, destination, chronology) I was able to distinguish various expressions of bilingualism: translation, transliteration, code-switching, juxtaposition of the two languages, isolated Greek words in Latin contexts and vice versa. The relationship between Latin and Greek is detectable through both epigraphic and linguistic style (layout, hierarchy, lexicon, morphological and phonetic facets).
Comparative Research of FPN and MTCN in Face Attribute Recognition

Face attributes recognition is a key process of understanding human face images in the field of computer vision. Face attributes come in different size and shape. Therefore, in the earlier work, they just collectively improve recognition accuracies with some attributes have higher accuracies and some do not. FPN is commonly used for detecting objects at different scales. In this paper, we combine this practical skill on human face attribute recognition. Compared to the common MTCN method, our method gains a competitive performance especially on those complex attributes.
Privacy Token Technique for Protecting User’s Attributes in a Federated Identity Management System for the Cloud Environment

Once an individual employs the use of the Internet for accessing information; carrying out transactions and sharing of data on the Cloud, they are connected to diverse computers on the network. As such, security of such transmitted data is most threatened and then potentially creating privacy risks of users on the federated identity management system in the Cloud. Usually, User's attributes or Personal Identifiable Information (PII) are needed to access Services on the Cloud from different Service Providers (SPs). Sometime these SPs may by themselves violate user's privacy by the reuse of user's attributes offered them for the release of services to the users without their consent and then carrying out activities that may appear malicious and then causing damage to the users. Similarly, it should be noted that sensitive user's attributes (e.g. first name, email, address and the likes) are received in their original form by needed SPs in plaintext. As a result of these problems, user's privacy is being violated. Since these SPs may reuse them or connive with other SPs to expose a user's identity in the cloud environment. This research is motivated to provide a protective and novel approach that shall no longer release original user's attributes to SPs but pseudonyms that shall prevent the SPs from violating user's privacy through connivance to expose the user's identity or other means. The paper introduces a conceptual framework for the proposed user's attributes privacy protection in a federated identity management system for the cloud. On the proposed system, the use of pseudonymous technique also called Privacy Token (PT) is employed. The pseudonymous technique ensures users' original attributes values are not sent directly to the SP but auto generated pseudo attributes values. The PT is composed of: Pseudo Attribute values, Timestamp and SP_ID. These composition of the PT makes it difficult for the User's PII to be revealed and further preventing the SPs from being able to keep them or reuse them in the future without the user's consent for any purpose. Another important feature of the PT is its ability to forestall collusion among several collaborating service providers. This is due to the fact that each SP receives pseudo values that have no direct link to the identity of the user. The prototype was implemented with Java programming language and its performance tested on CloudAnalyst simulation.
Privacy Preserving Big data Using Combine Anonymization and Encryption Approach

In recent years, data breach and sensitive information leak is main issue created. Data analysis is done on large amount of data to integrate and analysed data. But it increase data violation in data. To overcome this privacy is need of any particular data and it plays a vital role. Anonymization is a technique which hide the sensitive information of that particular data using generalization and suppression. It increases the privacy of any information. But it is not sufficient as in today's world many tools are available to break privacy.so we can use protection too to protect the data and by providing dual approach. For this various algorithms are tested among them Paillier encryption proved to be more efficient.
Hierarchy construction and classification of heterogeneous information networks based on RSDAEf

Heterogeneous information networks (HINs) composed of multiple types of nodes and links, play increasingly important roles in real life applications. Classification of the related data is an essential work in network analysis. Existing methods can effectively solve these classification tasks when they are applied to homogeneous information networks and simple data, but not for the noisy and sparse data. To address the problem, we propose Stacked Denoising Auto Encoder (SDAE) with sparse factors to learn features of nodes in heterogeneous networks. In particular, sparse factors are added in each hidden layer of the proposed stacked denoising auto-encoder to efficiently extract features from noisy and sparse data. Moreover, a relax strategy is employed to construct class hierarchy with high-quality based. Finally, nodes of the heterogeneous information network can be classified. Our proposed framework Relax strategy on Stacked Denoising Auto Encoder with sparse factors (RSDAEf) comparison with several existing methods clearly indicates RSDAEf outperforms the existing methods and achieves a classification precision of 88.3% on DBLP dataset.
An English Handwriting Evaluation Algorithm Based on CNNs

English handwriting evaluation is an essential part in elemental English teaching. An automatic evaluation algorithm for English handwriting quality is proposed in this paper. Generally, conventional document image processing approaches rely on hand-crafted features for capturing statistical or structural information. In contrast, we take advantage of Convolutional Neural Networks (CNNs) for extracting features from raw image pixels. The performance of this algorithm is more effective than traditional machine learning methods and the accuracy is greater than 94% in our experiment. Based on this algorithm, an intelligent English handwriting marking system is designed and it is already online.
Image Encryption Using Fractional Order Linear-Nonlinear Chaos

Abstract A fractional order chaos system is first designed. It is a linear-nonlinear hybrid using fractional order logistic map. The fractional order chaos system is closer to the real nature phenomena and has better cryptography features than the integer order chaotic systems in dynamics, such as larger range of key space and almost no periodic windows in bifurcation diagrams. Then, we propose an image encryption scheme employing the superior chaotic features of the fractional order chaos system. Which chaotic sequences generated are used for permutation and addition/subtraction operation of encryption depends on the plaintext image. Security analysis and test results indicate that our encryption scheme has a high efficiency and superior security.
Crossmodal attentive skill learner: learning in Atari and beyond with audio–video inputs

This paper introduces the Crossmodal Attentive Skill Learner (CASL), integrated with the recently-introduced Asynchronous Advantage Option-Critic architecture [Harb et al. in When waiting is not an option: learning options with a deliberation cost. arXiv preprint arXiv:1709.04571, 2017] to enable hierarchical reinforcement learning across multiple sensory inputs. Agents trained using our approach learn to attend to their various sensory modalities (e.g., audio, video) at the appropriate moments, thereby executing actions based on multiple sensory streams without reliance on supervisory data. We demonstrate empirically that the sensory attention mechanism anticipates and identifies useful latent features, while filtering irrelevant sensor modalities during execution. Further, we provide concrete examples in which the approach not only improves performance in a single task, but accelerates transfer to new tasks. We modify the Arcade Learning Environment [Bellemare et al. in J Artif Intell Res 47:253–279, 2013] to support audio queries (ALE-audio code available at https://github.com/shayegano/Arcade-Learning-Environment), and conduct evaluations of crossmodal learning in the Atari 2600 games H.E.R.O. and Amidar. Finally, building on the recent work of Babaeizadeh et al. [in: International conference on learning representations (ICLR), 2017], we open-source a fast hybrid CPU–GPU implementation of CASL (CASL code available at https://github.com/shayegano/CASL).
Virtual reality as an instrument of computer visualization

The paper is devoted to the development of computer visualization systems based on virtual reality. Some of these systems are designed for scientific visualization and some of them are designed for software visualization. The systems of scientific visualization are used for tasks of “pure mathematics”, computational mathematics, chemistry and biology. The systems of software visualization are used for visual programming, visualization of program objects, and performance evaluation of supercomputers. The tasks of interfaces for such systems are considered. Also the problems of the human factor for virtual reality environments are considered.
Information Context of Culture in Focus of Professional Discussion

The article presents an overview of the all-Russian scientific and practical conference (with international participation) “Information Context of Culture: Resources, Technologies, Service” (the 11th conference of heads and specialists of information services for culture and art). The conference, orga­nized by the Russian State Library, the Russian State Library of Arts, and the St. Petersburg State Institute of Culture, on the instructions of the Ministry of Culture of the Russian Federation, was held in Moscow on September 24—25, 2019. Its purpose was to summarize the results of scientific research and practical experience on the issues of information support of the cultural sector in the digital age, to identify some problems and find ways to overcome them, and to demonstrate the best achievements in this field. The conference was attended by more than 90 representatives of cultural management bodies, federal, regional and municipal libraries, universities, training centers and other organizations of culture, science, education and mass media from 22 regions of Russia, as well as heads and specialists of national libraries of Armenia, Belarus and Kazakhstan (via video link). Thanks to the online broadcast, the total audience of the event increased by almost 200 people. The conference participants noted the importance of scientific and information activities in the cultural sector both for society in general and for the sector’s development in particular. In order to improve this work, the conference participants recommended to promote its digitalization, which is aimed at creating public resources for culture and art and organizing information services in the 7/24 mode via the Internet, including on mobile devices; to intensify the mutual use of resources through participation in joint projects; and to strictly observe copyright when creating information resources and methodological materials.
A task-oriented dialogue bot using long short-term memory with attention for Thai language

A task-oriented dialogue bot helps users achieve a predefined goal within a closed domain. A neural-network based dialogue bot tracks the user intention in each action, which can reach promising performance compared to a hand-crafted baseline [1] and has a more flexible conversational flow. One such end-to-end architecture is the Hybrid Code Networks (HCNs) [2]. It uses the simulated conversation of human-bot in the domain of restaurant booking to train an LSTM to track dialogue states and predict the next bot response. This research proposes a similar architecture to HCNs with the addition of attention to LSTM [3]. The best results are obtained by our model on both original and Thai translated versions of bAbI task 5.