Reviewerly/exHarmony huggingface dataset:
Exploring the Role of Context to Distinguish Rhetorical and Information-Seeking Questions

Social media posts often contain questions, but many of the questions are rhetorical and do not seek information. Our work studies the problem of distinguishing rhetorical and information-seeking questions on Twitter. Most work has focused on features of the question itself, but we hypothesize that the prior context plays a role too. This paper introduces a new dataset containing questions in tweets paired with their prior tweets to provide context. We create classification models to assess the difficulty of distinguishing rhetorical and information-seeking questions, and experiment with different properties of the prior context. Our results show that the prior tweet and topic features can improve performance on this task.
Nested-Wasserstein Self-Imitation Learning for Sequence Generation

Reinforcement learning (RL) has been widely studied for improving sequence-generation models. However, the conventional rewards used for RL training typically cannot capture sufficient semantic information and therefore render model bias. Further, the sparse and delayed rewards make RL exploration inefficient. To alleviate these issues, we propose the concept of nested-Wasserstein distance for distributional semantic matching. To further exploit it, a novel nested-Wasserstein self-imitation learning framework is developed, encouraging the model to exploit historical high-rewarded sequences for enhanced exploration and better semantic matching. Our solution can be understood as approximately executing proximal policy optimization with Wasserstein trust-regions. Experiments on a variety of unconditional and conditional sequence-generation tasks demonstrate the proposed approach consistently leads to improved performance.
Simpler Proofs of Quantumness.

A proof of quantumness is a method for provably demonstrating (to a classical verifier) that a quantum device can perform computational tasks that a classical device with comparable resources cannot. Providing a proof of quantumness is the first step towards constructing a useful quantum computer. There are currently three approaches for exhibiting proofs of quantumness: (i) Inverting a classically-hard one-way function (e.g. using Shor’s algorithm). This seems technologically out of reach. (ii) Sampling from a classically-hard-to-sample distribution (e.g. BosonSampling). This may be within reach of near-term experiments, but for all such tasks known verification requires exponential time. (iii) Interactive protocols based on cryptographic assumptions. The use of a trapdoor scheme allows for efficient verification, and implementation seems to require much less resources than (i), yet still more than (ii). In this work we propose a significant simplification to approach (iii) by employing the random oracle heuristic. (We note that we do not apply the Fiat-Shamir paradigm.) We give a two-message (challenge-response) proof of quantumness based on any trapdoor claw-free function. In contrast to earlier proposals we do not need an adaptive hard-core bit property. This allows the use of smaller security parameters and more diverse computational assumptions (such as Ring Learning with Errors), significantly reducing the quantum computational effort required for a successful demonstration.
Persepsi inter-aktivitas khalayak terhadap film interaktif “Bandersnatch”

Interactive stories that were developed in video games and storybooks are now applied to films. With the interaction concept in a film, the audience would not be enjoying linear narrative, but also could help in determining the storyline according to the available choices. The interactive story wants to obscure the passive and active action in audio-visual technology andcreate an intense interaction with humans and the computers to grow stronger emotions between the audience and the movie character or the story. This study used qualitative methods with interview techniques for each person regarding third-order dependency, social presence, and emotions that are built when watching the interactive movie, “Bandersnatch”. The experience of watching while participate in choosing the storyline, build an intense interaction, satisfaction and made humans as audiences feel closer and feel the emotions of the movie character.
Economic Prospect Theory Applied to Cybersecurity

A growing concern in the cybersecurity community evaluation of the strengths and defenses regarding cyberattacks. One approach that has not been often explored is to estimate the strength of an attack or defense in economic terms. For example, estimation of the memory required for code used for an attack, or what is equivalent, the computer time to execute an attack. We choose to express the costs in economic terms, and thus define the method of analyzing an important line of research known as “behavioral economics”, pioneered by Kahneman and Tversky, and translated into cybersecurity terms. In this way we attempt to determine a cybersecurity analog for well-known results in economic prospect theory to be able to estimate the costs of cyberattacks and defenses.
Feature Extraction Techniques for Human Emotion Identification from Face Images

Emotion recognition has been one of the stimulating issues over the years due to the irregularities in the complexity of models and unpredictability between expression categories. So many Emotion detection algorithms have developed in the last two decades and still facing problems in accuracy, complexity and real-world implementation. In this paper, we propose two feature extraction techniques: Mouth region-based feature extraction and Maximally Stable Extremal Regions (MSER) method. In Mouth based feature extraction method mouth area is calculated and based on that value the emotions are classified. In the MSER method, the features are extracted by using connecting components and then the extracted features are given to a simple ANN for classification. Experimental results shows that the Mouth area based feature extraction method gives 86% accuracy and MSER based feature extraction method outperforms it by achieving 89% accuracy on DEAP. Thus, it can be concluded that the proposed methods can be effectively used for emotion detection.
Development of mobile application upon mechanical engineering students’ learning styles

Abstract The use of technology in this era of globalisation is growing from day to seconds. Mobile learning or also known as m-learning is commonly heard lately as we are in the 21st century of education. Mobile applications developed are to enhance m-learning among students. Henceforth, making students engaged to m-learning is only possible when we had identified their learning styles. Current paper presents the development of mobile application for Polytechnic Mechanical Engineering Students (PolyMES) based on the previous analysis of learning styles among premier polytechnic mechanical engineering students in Malaysia. The aim of this study is achieved whereby the design for PolyMES is developed according to these mechanical engineering students’ learning style. The mobile application developed requires the understanding of students’ learning style to enhance m-learning. PolyMES is designed to be user friendly application that enables students to study the subject at their own space. They can read the simple notes with related video and images. In addition, students are able to test themselves with the quiz and exercises given and also view the list of formulas and symbols with sample calculation as a practice. The features available in PolyMES designed to suit students’ different types of learning styles. As known every individual has their own learning styles of retaining new information and skills. Hereby, PolyMES design is developed to cater students using smart phones at various learning style and might be useful for further implementations of m-learning.
Enhancing Medical Education with Data-Driven Software: The TrainCoMorb App.

Medical education can take advantage of big data to enhance the learning experience of students. This paper describes the development of TrainCoMorb, an online, data-driven application for medical students who can practice recognizing comorbidities and their attributable risk for negative outcomes. Trainees access TrainCoMorb to create scenarios of comorbidities, step-by-step, and see snapshots of the risk for inpatient death, hospital septicemia and the projected length of stay. The study utilized an enormous claims dataset (N=11m.). A dynamic Bayesian algorithm was developed, which calculates and updates conditional probabilities for the outcomes under study in each phase of an ongoing scenario. The trainee initiates a scenario by selecting demographics and a principal diagnosis, then adds chronic and hospital-acquired conditions to see a summary of the attributable risk in each phase. TrainCoMorb is anticipated to assist medical students gain a better understanding of comorbidities and their impact on clinical outcomes.
About the need to change the content of professional training of a future informatics teacher in artificial intelligence

The article examines the current state of teacher training in artificial intelligence. The content of disciplines related to artificial intelligence included in the curricula of pedagogical universities of Russia is analyzed. The current trends in the selection of software tools used for developments in artificial intelligence technologies in Russia and abroad are considered. The objective of the article is to substantiate the need to review the content of the training of a future informatics teacher in artificial intelligence, taking into account current trends in the development of artificial intelligence technologies and the requirements of Federal State Educational Standards. From studying expert systems, the Prolog and Lisp languages, the author suggests moving on to studying modern tools for implementing artificial intelligence technologies using the Python programming language. The article provides the content of the discipline “Technology of Artificial Intelligence” proposed by the author, being tested at the Novosibirsk State Pedagogical University. Such a change in the content is intended to ensure the practical orientation of the educational process within the framework of the relevant discipline, to increase the competence of the future informatics teacher in the field of modern high tech information technologies, to promote the development of motivation to learn the current Python programming language, thereby increasing its competitiveness in the labor market.
Comparative Study of the Ultrasonic and Infrared Person Counter

Nowadays, every organization is totally depend upon the technologies and the now working on the concept of the automation means, it means that every day work will perform by the machine automatically. So, the error chances may be less and the work will be done in effective manner as well as in short range of time. Some of daily task of any organization is to calculate the person who have entered or exit from the organization or any building. So, in this, we discuss about the person counter which is widely used by the organization for counter. We also define the methodology on which the principal of the counter works as well as we also discuss various technologies used to count the person count. In this paper, we also compare the technologies. This paper will provide the comparison on the ultrasonic and infrared sensor-based counter.
A Technique and Method for Mixed Reality Performance with Digital Augments, Gestural Data, and Living Plants

We propose a new method, technique, and practices for performing with mixed reality environments and technologies, informed by embodiment and electro acoustics, and critically underscored by new materialism and posthumanism. Through the Contact projects, Wright and Howden explore a practice-based approach to live performance in mixed reality, through a novel method that combines gestural controllers, living plants and digital augments. This research investigates potential for multimodal performance in mixed reality (MR), using a novel technical set-up that combines of the HTC Vive with a Leap Motion mount, the MIDI Sprout interface for sensing the bio-electrical signals of plants, transposed to visual data in Touch Designer, and Unity 3D for generating digital augments.
ERP Curriculum Innovation System Based on Big Data and Information Technology

Abstract The adjustment of national industrial structure is in urgent need of a large number of talents with innovative and application-oriented ability. For the major of financial management, enterprises need graduates with strong comprehensive ability. Therefore, many universities have started ERP sand table simulation training courses. This experiential teaching mode enables students to be involved in the operation of virtual enterprises. Through the research of ERP curriculum innovation system based on big data and information technology, this paper provides technical Suggestions for the optimization of ERP curriculum. This article first introduced the ERP course about the content and construction of ERP course system used in the algorithm, based on the theory, on the basis of our simulation has set up a platform of ERP teaching, and for two months in a university teaching experiment, the collection of the student for simulation platform of ERP teaching evaluation, the results showed that 83.2% of students satisfied with the innovation of the course. Through the study of this course, students can further master the skills of entrepreneurial team building, strengthen the concept of cost, and hone the sense of competition.
Improved difference model applied in the Fourier-transform-based integration method based on Taylor theory

The two-dimensional Fourier-transform-based integration algorithm is widely used in shape or wavefront reconstruction from gradients. However, its reconstruction accuracy is limited by the truncation error of the difference model. The truncation error is affected by the distribution of the sampling points. It increases when the sampling points are unevenly distributed and arranged irregularly. For improving, a novel way to calculate the difference is proposed based on Taylor expansion theory of binary functions. The first-order partial derivative terms are used to estimate the second- and third-order partial derivative terms for reducing the truncation error. The proposed difference model is applied to Fourier-transform-based integration. The reconstruction results show that it can get better results when the sampling points are irregularly distributed.
Signatures of Knowledge for Boolean Circuits Under Standard Assumptions

This paper constructs unbounded simulation sound proofs for boolean circuit satisfiability under standard assumptions with proof size $$O(n+d)$$ bilinear group elements, where d is the depth and n is the input size of the circuit. Our technical contribution is to add unbounded simulation soundness to a recent NIZK of González and Ràfols (ASIACRYPT'19) with very small overhead. Our new scheme can be used to construct the most efficient Signature-of-Knowledge based on standard assumptions that also can be composed universally with other cryptographic protocols/primitives.
Method of Highway Risk Assessment and Accident Quantity Prediction Based on Multi-Source Heterogeneous Data and Deep Neural Network

In this paper, using the multi-source data, combined with deep neural network, an automatic assessment model for highway risk and accident number prediction model are established. First, based on the multi-source heterogeneous data collected by different monitoring facilities and sensors on the highway, the features are fused. Secondly, the shuffled frog leaping algorithm is used to optimize the features to reduce the dimension. Thirdly, we improve the traditional deep neural network by connecting the output of each layer of neural network to the last fully connected layer to obtain a fused vector. Finally, the effectiveness of the proposed algorithm is verified in the experimental results.
Preventing Neural Network Weight Stealing via Network Obfuscation

Deep Neural Networks are robust to minor perturbations of the learned network parameters and their minor modifications do not change the overall network response significantly. This allows space for model stealing, where a malevolent attacker can steal an already trained network, modify the weights and claim the new network his own intellectual property. In certain cases this can prevent the free distribution and application of networks in the embedded domain. In this paper, we propose a method for creating an equivalent version of an already trained fully connected deep neural network that can prevent network stealing, namely, it produces the same responses and classification accuracy, but it is extremely sensitive to weight changes.
The Relationship between Risk-Behaviors to Exposure Toward Endocrine Disrupting Chemicals and Dysmenorrhea in Female University Students

This descriptive study was designed to examine the relationship between risk-behavior to exposure toward endocrine disrupting chemicals(EDCs) and dysmenorrhea in young women. Data were collected from 236 university female students using self-reported questionnaire about risk-behavior to exposure toward EDCs and dysmenorrhea. Data analysis method were correlation analysis, factor analysis and ANOVA. As results, mean for risk-behavior to exposure toward EDCs was 2.70±0.41 and mean of dysmenorrhea was 2.80±1.07. The relationship between risk-behavior to exposure toward EDCs and dysmenorrhea showed significant correlation(r=.16, ρ=.012). 18 risk-behavior items to exposure toward EDCs were sorted into four factors by factor analysis: plastic container usage, exposure to pollutants, fast food and canned drink intake, chemical detergent usage. In two of four factor, dysmenorrhea showed significant differences among three level of risk-behavior to increase exposure toward EDCs. These results suggest that riskbehavior to increase exposure toward EDCs is related with dysmenorrhea.
Model-Driven Web Page Segmentation for Non Visual Access

Web page segmentation aims to break a large page into smaller blocks, in which contents with coherent semantics are kept together. Within this context, a great deal of approaches have been proposed without any specific end task in mind. In this paper, we study different segmentation strategies for the task of non visual skimming. For that purpose, we propose to segment web pages into visually coherent zones so that each zone can be represented by a set of relevant keywords that can be further synthesized into concurrent speech. As a consequence, we consider web page segmentation as a clustering problem of visual elements, where (1) a fixed number of clusters must be discovered, (2) the elements of a cluster should be visually connected and (3) all visual elements must be clustered. Therefore, we study variations of three existing algorithms, that comply to these constraints: K-means, F-K-means, and Guided Expansion. In particular, we evaluate different reading strategies for the positioning of the initial K seeds as well as a pre-clustering methodology for the Guided Expansion algorithm, which goal is to (1) fasten the clustering process and (2) reduce unbalance between clusters. The performed evaluation shows that the Guided Expansion algorithm evidences statistically increased results over the two other algorithms with the variations of the reading strategies. Nevertheless, improvements still need to be proposed to increase separateness.
Proposal for Named Entities Recognition and Classification (NERC) and the Automatic Generation of Rules on Mexican News

In this paper we introduce a proposal for extracting facts from news on Mexican online newspapers through their RSS (Really Simple Syndication). This problem will be addressed by using the task of automatic named entities recognition and classification (NERC), as well as the semantic relation extraction among entities, so that we can build a database of facts and rules from the obtained entities in an automatic manner. The final aim is to be able to infer new rules through the use of the knowledge databases constructed and an inference engine. In order to build the NER model, we performa manual annotation of corpora with different tags that include the baseline tags (person names, organizations, locations, dates and numeral). The idea proposed is presented in this paper with an example scenario together with the procedure employed for solving the problem of automatic inference of new rules.
Analysis of Automatic Annotations of Real Video Surveillance Images

The results of the analysis of the automatic annotations of real video surveillance sequences are presented. The annotations of the frames of surveillance sequences of the parking lot of a university campus are generated. The purpose of the analysis is to evaluate the quality of the descriptions and analyze the correspondence between the semantic content of the images and the corresponding annotation. To perform the tests, a fixed camera was placed in the campus parking lot and video sequences of about 20 minutes were obtained, later each frame was annotated individually and a text repository with all the annotations was formed. It was observed that it is possible to take advantage of the properties of the video to evaluate the performance of the annotator and the example of the crossing of a pedestrian is presented as an example for its analysis.
AMISA: A Pilot Study of an Emotional Supporting Device Between Friends Over Long-Distance

Under the rigid regulation of Chinese 985-Project sponsored university entrance exam for 2.09% in 2018, the new generation of Chinese teenagers usually have to leave their province to another city which is thousand miles away from home. This means that these young adults have to leave familiar family support network and deal with homesickness earlier than other countries. Such lonely experience will affect these young adults who usually grew up as the middle class in the modern Chinese society. Friendship is the only support these young adults will get. And the hug from friends are their most missed interactions. Our research intends to develop an interactive device that can transfer their experience into a supporting comfort system that can provide warm awareness and inter-personal relationship. This paper reports our pilot study: an eMotional Supporting Awareness system (AMISA) as a haptic affect support system that supports affective communication with friends over long-distance. The approach of this study is user centered design, through user interviews and literature reviews, we defined the user requirements and found corresponding technology in HCI to prototype the conceptual design. We developed AMISA from three dimensions: (1) affective analysis; (2) mediated social touch; (3) user experience. Subsequently, we conducted a user evaluation of the prototype, AMISA is used as a data collector to observe user interactions for iterating subsequent design outcomes.