Reviewerly/exHarmony huggingface dataset:
Task-Oriented Multi-Modal Question Answering For Collaborative Applications

Cobots that can work in human workspaces and adapt to human need to understand and respond to human’s inquiry and instruction. In this paper, we propose new question answering (QA) task and dataset for human-robot collaboration on task-oriented operation, i.e., task-oriented collaborative QA (TCQA). Differing from conventional video QA for answering questions about what happened in video clips constrained by scripts and subtitles, TC-QA aims to share common ground for task-oriented operation through question answering. We propose an open-end (OE) format of answer with text reply, image with annotated related objects, and video with operation duration to guide operation execution. Designed for grounding, the TC-QA dataset comprises query videos and questions to seek acknowledgement, correction, attention to task-related objects, and information on objects or operation. Due to the flexibility of real-world task with limited training sample, we propose and evaluate a baseline method based on a hybrid approach. The hybrid approach employs deep learning methods for object detection, hand detection and gesture recognition, and symbolic reasoning to ground question on observation for providing the answer. Our experiments show that the hybrid method is effective for the TC-QA task.
Big Data Encryption Technology Based on ASCII And Application On Credit Supervision

Big Data Platform provides business units with data platforms, data products and data services by integrating all data to fully analyze and exploit the intrinsic value of data. Data accessed by big data platforms may include many users' privacy and sensitive information, such as the user's hotel stay history, user payment information, etc., which is at risk of leakage. This paper first analyzes the risks of data leakage, then introduces in detail the theoretical basis and common methods of data desensitization technology, and finally puts forward a set of effective market subject credit supervision application based on asccii, which is committed to solving the problems of insufficient breadth and depth of data utilization for enterprises involved, the problems of lagging regulatory laws and standards, the problems of separating credit construction and market supervision business, and the credit constraints of data governance.
The Language of Food: Carving out a Place for Food Studies in Language Curricula

This chapter argues for the place of food studies in tertiary language studies programs. With a myriad of changes to education throughout the twentieth century, language study lost its eminent position as a gateway to higher learning, which means we are required to articulate our relevance to students and university governance. Food and food culture have great appeal amongst students and carving out a place for food studies in our language curricula allows us to generate a new interest amongst a changed student cohort. As well as providing students with an enriching way of learning about other cultures, the non-canonical and universal phenomenon of food or food discourse has the advantage of being immediately accessible to our students who all have their own experiences of food. The study of food also provides us with an opportunity to enhance students’ intercultural skills, which have increasing value in the global workplace. Understanding the multiple layers of meaning attached to food and food culture helps students to develop a sensitivity to the importance of the everyday in their interactions with other cultures. We will discuss this synergy between languages and food studies in the context of tertiary language studies in Spanish and Italian, detailing some of the initiatives in this area.
Learning Low-Rank Structured Sparsity in Recurrent Neural Networks

Acceleration and wide deployability in deeper recurrent neural network is hindered by high demand for computation and memory storage on devices with memory and latency constraints. In this work, we propose a novel regularization method to learn hardware-friendly sparse structures for deep recurrent neural networks. Considering the consistency of dimension in continuous time units in recurrent neural networks, low-rank structured sparse approximations of the weight matrices are learned through the regularization without dimension distortion. Our method is architecture agnostic and can learn compact models with higher degree of sparsity than the state-of-the-art structured sparsity learning method. The structured sparsity rather than random sparsity also facilitates the hardware implementation. Experiments on language modeling of Penn TreeBank dataset show that our approach can reduce the parameters of stacked recurrent neural network model by over 90% with less than 1% perplexity loss. It is also successfully evaluated on larger highway neural network model with word2vec dataset like enwik8 and text8 using only 20M weights.
A Framework for Organization-Centered Doxastic Reasoning

Organizational structures control goal-directed activities of companies or groups in an efficient manner. These activities are typically described using rules, roles, and responsibilities. In this paper we focus on doxastic reasoning of groups and individuals in organizations situated in dynamic environments. We model organizational structures using partial orders what is general enough to serve our purposes. In particular, we show how belief bases associated with organizational units/entities are to be structured to reflect the induced hierarchies. The main logical tool ensuring efficiency is belief shadowing, a relatively recent approach to transient belief change. As the main contribution, we develop Drocs, a computationally friendly framework for organization-centered doxastic reasoning. An illustrative case study is also provided.
Advanced Manufacturing Documentation

This paper examines documentation procedures and rhetorical contexts navigated by technical communicators in postindustrial, advanced manufacturing environments. Also addressed are the challenges of structure and authority faced by the technical writer in postindustrial hierarchies, particularly those of a STEM-dominated field. In reference to produced documentation, the need for technical communicators to explore alternative formats is such that the traditional operating manual may not be sufficient to fully account for the complexities of automated and digitally-augmented manufacturing systems. Documentation processes, then, must account for collaborative, distributed work approaches, as well as those of technology transfer in the composition and distribution of documentation in support of postindustrial, advanced manufacturing
Expressiveness of SETAFs and support-free ADFs under 3-valued semantics

AbstractGeneralising the attack structure in argumentation frameworks (AFs) has been studied in different ways. Most prominently, the binary attack relation of Dung frameworks has been extended to the notion of collective attacks. The resulting formalism is often termed SETAFs. Among the generalisations of AFs, abstract dialectical frameworks (ADFs) allow for a systematic and flexible generalisation of AFs in which different kinds of logical relations, e.g. attack and support, among arguments can be represented. Restricting the logical relations among arguments leads to different subclasses of ADFs of interest. In this work, we consider so-called support-free ADFs that allow for all kinds of attacks but no support or other relations and SETADFs that embed SETAFs in the ADF setting. The aim of this paper is to shed light on the relation between these two different approaches. To this end, we investigate and compare the expressiveness of SETAFs and support-free ADFs under the lens of 3-valued semantics. Our results show that it is only the presence of unsatisfiable acceptance conditions in support-free ADFs that discriminates the two approaches.Keywords: Abstract argumentation frameworksabstract dialectical frameworkscollective attack Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 We note this concept does not allow for auxiliary arguments that simulate certain behaviours, see e.g. Modgil and Bench-Capon (Citation2011).2 As discussed in Polberg (Citation2017), in general, SETAFs translate to bipolar ADFs that contain attacking and redundant links. However, when we first remove redundant attacks from the SETAF we obtain a SFADF.3 Recent work Dvořák et al. (Citation2021a) studies different types of symmetry in SETAFs. Our notion of symmetry corresponds the notion of primal-symmetric SETAFs without self-attacks in Dvořák et al. (Citation2021a)Additional informationFundingThis research has been supported by Austrian Science Fund (FWF) through projects I2854, P30168. The second researcher is supported by 1- the Netherlands Organisation for Scientific Research (NWO) through the Hybrid Intelligence Gravitation Programme, And 2- the Netherlands eScience Center project “The Eye of theBeholder”.
Logo Based Amphetamines Classification using SURF and Bag-of-features model

In this paper, we propose a framework for classifying the top view image of amphetamines based on their logo using SURF and Bag-of-features model. During our experiment, we found that the unsmooth surface of amphetamines and low contrast are the main factors of low accuracy for classification. Therefore, we propose a process to enhance the main feature and reduce noise on the surface using adaptive filter, Contrast-Limited Adaptive Histogram Equalization (CLAHE), active contour and image morphology. The result from our proposed preprocess algorithm shows that the clarity of the logo on amphetamines is improved and the noise is reduced. We also then apply SURF to extract features and classify using Bag-of-features model. This experimental result shows that our proposed preprocess for each step can improve the accuracy up and the accuracy of our method up to 97 percent.
Temporal Power Redistribution as a Countermeasure against Side-Channel Attacks

Side channel analysis attacks are considered an extreme hardware security hazard for cryptographic devices. There are numerous approaches to prevent attackers from extracting useful information from secured devices. Nonetheless the cost of implementing an effective countermeasure is usually very high in terms of area/performance. In this paper we propose a novel approach to the temporal redistribution of the power information. Specifically, we present a circuit level methodology that makes it possible to manipulate the three main parameters of the current profile during the clock period: the start time of the computation, the duration and the amplitude. The effectiveness of the proposed countermeasure was evaluated on a 4-bit cryptographic function in a 65nm TSMC process. The simulation results indicate that the number of secret bits that leaked from the protected design (i.e., the mutual information) was reduced dramatically from 4 bits to 0.85 bits. In addition, at least 1500 ideal noise-free power traces were required to extract these bits, whereas less than 150 traces were required to extract the whole 4 bits from the unprotected design. The sensitivity of the protected circuit to process and environmental variations are minimal, with measured standard deviation of 0.1bit. The area overhead is up to 32%.
Leveraging Federated Clouds for Improving Blockchain Performance

Today, blockchains are popular due to their promise of maintaining a tamper-proof and reliable distributed ledger. As more and more organizations are trying to incorporate this technology within their applications, they are finding the performance and scalability of these systems to be significant bottlenecks. The emerging federated clouds, with a conglomeration of autonomous clouds, could be a solution for this problem. Integrating federated clouds within a blockchain can help overcome these bottlenecks without undermining the autonomy and the peer-to-peer characteristic of the underlying systems. In this paper, we identify a few of the roles in which a federated cloud can play an effective role in a blockchain. In particular, we focus on its role in reducing the communication cost, in cooperation with the miners, incurred in propagating the mined blocks to all the nodes in the system. The proposed integrated approach results in reduced communication cost and improved time for synchronization of the distributed ledger at different nodes. The reduction in network traffic and latency in block synchronization is achieved without deviating from the fundamental autonomous nature of blockchains. The proposed approach results in higher transaction throughputs and lower query response times.
Performing Arithmetic Using a Neural Network Trained on Digit Permutation Pairs

In this paper a neural network is trained to perform simple arithmetic using images of concatenated handwritten digit pairs. A convolutional neural network was trained with images consisting of two side-by-side handwritten digits, where the image's label is the summation of the two digits contained in the combined image. Crucially, the network was tested on permutation pairs that were not present during training in an effort to see if the network could learn the task of addition, as opposed to simply mapping images to labels. A dataset was generated for all possible permutation pairs of length 2 for the digits 0–9 using MNIST as a basis for the images, with one thousand samples generated for each permutation pair. For testing the network, samples generated from previously unseen permutation pairs were fed into the trained network, and its predictions measured. Results were encouraging, with the network achieving an accuracy of over 90% on some permutation train/test splits. This suggests that the network learned at first digit recognition, and subsequently the further task of addition based on the two recognised digits. As far as the authors are aware, no previous work has concentrated on learning a mathematical operation in this way.
Sylvian-Silva (SFORCE): An Ensembled Boost Approach Towards Machine Learning

Gathering a minimalistic set of convincing evidence for a logical conclusion formed the premises of Occam's Razor, put forth by Libert Froidmont. Occam's Razor, although sound and appealing for theoretical proofs, is speculative and vague when it comes to practical scientific experiments with multivariate big data sets. The advent of the "Principle of multiple explanations" forms the basis of all ensemble architectures for Machine Learning. Random forests are an ensemble of decision trees that provide excellent results among its competitors. The main disadvantages of a random forest are the computational cost and the curse of overfitting. This paper presents Sylvian Silva (SFORCE), an Ensembled Boost approach towards Machine Learning that combines the strength of Random Forest ensemble and the boosting approaches. Stagewise Additive Modeling and real-valued confidence related predictions for Multiclass classification and regression form the basis of these boosting algorithms. Boosting ensures that the algorithm gathers the inherent characteristics and has the capability to predict future targets. The SFORCE algorithm's performance is compelling across six different data sets. SFORCE reports high accuracy levels consistently without overfitting when compared with 17 different machine learning algorithms for classification and 10 different algorithms for regression.
Improving writing skills of eleventh-grade students by writing recount text through a field learning experience strategy

It is typically found that there are still many English teachers who cannot implement a proper strategy for teaching writing. The most crucial problem is that teachers lack an effective strategy to teach English writing skills in teaching the recounting of an event or experience. This research is focused on analyzing: (1) how to improve the teacher’s teaching performance; (2) how to improve students’ learning motivation and participation in the teaching–learning process; (3) how to improve students’ achievement in English writing skills, particularly in writing recount text through a field learning experience strategy. The purpose of the research is to find an effective strategy for teaching English writing. The research employed a classroom action research method in a cyclic design that was implemented in spiral mode covering planning, implementation, observation and reflection. The research instruments were observation checklists, field notes, documentation, and testing; data was collected through cycles 1 and 2, each cycle involving three meetings. The subject of research was 24 eleventh-grade students at SMA Negeri 7 Palu. The data were analyzed both qualitatively and quantitatively. The findings of the research revealed that: (1) the teacher’s teaching performance showed improvement from cycle 1 to cycle 2; (2) the students’ learning motivation and classroom participation increased significantly; (3) the students’ average score from cycle 1 was 63.7, and their classroom achievement was 45.8%; from cycle 2, their average score was 72.91 and their classroom achievement was 91.66%. The significance of the research is the increase in students’ achievement and motivation in learning English writing skills.
SelfLens

Independently selecting food items while shopping, or storing and cooking food items correctly can be a very difficult task for people with special needs. Product labels on food packaging contain an ever-increasing amount of information, which can also be in a variety of languages. The amount of information and also the features of the text can make it difficult or impossible to read, in particular for those with visual impairments or the elderly. Several tools or applications are available on the market or have been proposed to support this type of activity (e.g. barcode or QR code reading), but they are limited and may require the user to have specific digital skills. Moreover, repeatedly using an application to read the label contents can require numerous steps on a touch-screen, and consequently be time-consuming. In this work, a portable tool is proposed to support people in reading the contents of labels and acquiring additional information, while they are using the item at home or shopping at the supermarket. The aim of our study is to propose a simple portable assistive technology tool which 1) can be used by anyone, regardless of their digital personal skills 2) does not require a smartphone or complex device, 3) is a low-cost solution for the user.
Blockchain Driven Three Domain Secure 2.x in Digital Payment Services Architecture

Due to the recent advancements in digital commerce, consumers expect real-time digital payment convenient and available across channels as more connected devices become payment devices. It offers consumers to pay in-store or online purchases in many diversified ways. The three domains secure protocol evolved to version 2.x (3DS2) supporting the development in digital payment domain and its rapid adaptation. The specification includes the provisioning of the application-based purchases enabling risk-based decisions to authenticate the consumer transactions. 3DS2 enhances consumers' checkout experiences through out-of-band authentication. It eliminates the need for enrollment process and static password supporting non-payment activities and native mobile. The primary challenges to implement 3DS2 are dimensioning the risks, real-time variability in the risk factors, and precision to compute the accumulative risk associated with the individuals. Financial services, merchants, and consumers are enabled to connect into the blockchain network using application programming interfaces (APIs). It alleviates participants of Blockchain network from having to build out their own distributed transactions' server nodes. This paper proposes a blockchain-driven 3DS2 service architecture framework that integrates the risk-based decisions and provides a secure communication platform in digital commerce. We illustrate the increased level of authenticity, maintainability, extendibility, and flexibility in the digital payment ecosystem with the industry case study of membership-based in-store or online charitable contribution campaigns during point-of-sale.
Elastic parameters estimation using multi-model based on machine learning

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2020Elastic parameters estimation using multi-model based on machine learningAuthors: Kai XuZhentao SunShixing WangJinliang TangRuyi ZhangKai XuSinopec Geophysical Research InstituteSearch for more papers by this author, Zhentao SunSinopec Geophysical Research InstituteSearch for more papers by this author, Shixing WangSinopec Geophysical Research InstituteSearch for more papers by this author, Jinliang TangSinopec Geophysical Research InstituteSearch for more papers by this author, and Ruyi ZhangSinopec Geophysical Research InstituteSearch for more papers by this authorhttps://doi.org/10.1190/segam2020-3428172.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractReservoir characterization involves the estimation elastic parameters from well-log data and seismic data, nonlinear elastic parameters estimation based on machine learning is a popular method for reservoir characterization. The common process is to construct a comprehensive mapping relationship between seismic data including multi attributes and well data, but the prediction accuracy may be low when using one prediction model in different sedimentary environments. In order to solve this problem, we put forward the new method of multi-model prediction based on machine learning to estimate elastic parameters directly, in other words, we use corresponding deep neural network to estimate elastic parameters in different sedimentary environments. In this paper, our work mainly includes three parts: firstly, waveform classification based on Selforganization map, secondly, training and optimizing multi prediction models based on Long Short Term Memoryrecurrent neural networks, thirdly, elastic parameters estimation using multi-model. This method is successfully applied in model and real data, it shows the estimation results are more reasonable and effective.Presentation Date: Monday, October 12, 2020Session Start Time: 1:50 PMPresentation Time: 4:45 PMLocation: 351FPresentation Type: OralKeywords: machine learning, reservoir characterization, interpretation, seismic attributes, seismic stratigraphyPermalink: https://doi.org/10.1190/segam2020-3428172.1FiguresReferencesRelatedDetails SEG Technical Program Expanded Abstracts 2020ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2020 Pages: 3887 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 30 Sep 2020 CITATION INFORMATION Kai Xu, Zhentao Sun, Shixing Wang, Jinliang Tang, and Ruyi Zhang, (2020), "Elastic parameters estimation using multi-model based on machine learning," SEG Technical Program Expanded Abstracts : 1471-1475. https://doi.org/10.1190/segam2020-3428172.1 Plain-Language Summary Keywordsmachine learningreservoir characterizationinterpretationseismic attributesseismic stratigraphyPDF DownloadLoading ...