Reviewerly/exHarmony huggingface dataset:
Adversarial Mixing Policy for Relaxing Locally Linear Constraints in Mixup

Mixup is a recent regularizer for current deep classification networks.Through training a neural network on convex combinations of pairs of examples and their labels, it imposes locally linear constraints on the model's input space.However, such strict linear constraints often lead to under-fitting which degrades the effects of regularization.Noticeably, this issue is getting more serious when the resource is extremely limited.To address these issues, we propose the Adversarial Mixing Policy (AMP), organized in a "min-max-rand" formulation, to relax the Locally Linear Constraints in Mixup.Specifically, AMP adds a small adversarial perturbation to the mixing coefficients rather than the examples.Thus, slight non-linearity is injected in-between the synthetic examples and synthetic labels.By training on these data, the deep networks are further regularized, and thus achieve a lower predictive error rate.Experiments on five text classification benchmarks and five backbone models have empirically shown that our methods reduce the error rate over Mixup variants in a significant margin (up to 31.3%), especially in low-resource conditions (up to 17.5%).
Exophoric Pronoun Resolution in Dialogues with Topic Regularization

Resolving pronouns to their referents has long been studied as a fundamental natural language understanding problem.Previous works on pronoun coreference resolution (PCR) mostly focus on resolving pronouns to mentions in text while ignoring the exophoric scenario.Exophoric pronouns are common in daily communications, where speakers may directly use pronouns to refer to some objects present in the environment without introducing the objects first.Although such objects are not mentioned in the dialogue text, they can often be disambiguated by the general topics of the dialogue.Motivated by this, we propose to jointly leverage the local context and global topics of dialogues to solve the out-of-text PCR problem.Extensive experiments demonstrate the effectiveness of adding topic regularization for resolving exophoric pronouns.
MARMOT: A Deep Learning Framework for Constructing Multimodal Representations for Vision-and-Language Tasks

Political activity on social media presents a data-rich window into political behavior, but the vast amount of data means that almost all content analyses of social media require a data labeling step. However, most automated machine classification methods ignore the multimodality of posted content, focusing either on text or images. State-of-the-art vision-and-language models are unusable for most political science research: they require all observations to have both image and text and require computationally expensive pretraining. This paper proposes a novel vision-and-language framework called multimodal representations using modality translation (MARMOT). MARMOT presents two methodological contributions: it can construct representations for observations missing image or text, and it replaces the computationally expensive pretraining with modality translation. MARMOT outperforms an ensemble text-only classifier in 19 of 20 categories in multilabel classifications of tweets reporting election incidents during the 2016 U.S. general election. Moreover, MARMOT shows significant improvements over the results of benchmark multimodal models on the Hateful Memes dataset, improving the best result set by VisualBERT in terms of accuracy from 0.6473 to 0.6760 and area under the receiver operating characteristic curve (AUC) from 0.7141 to 0.7530.
Learning low-degree functions from a logarithmic number of random queries

We prove that every bounded function f:{−1,1}n→[−1,1] of degree at most d can be learned with L2-accuracy ε and confidence 1−δ from log(n/δ) ε−d−1 Cd3/2√logd random queries, where C>1 is a universal finite constant.
PVsyst Software for Better Energy Efficiency of a Grid-Connected Photovoltaic Power Station

The paper presents a classification of solar tracking systems used in photovoltaic power stations (PVS) and their operating principles. A simulation model of a grid-connected 5-kW PVS has been developed in PVsyst, to which end the researchers selected PVS equipment and optimized the PV cell tilt angles. The paper further analyzes a grid-connected PVS in Orenburg Oblast in PVsyst under the following configurations: static PV cells, not tilted vs seasonally varied tilts; single-axis solar trackers with vertical and horizontal axes of rotation vs a dual-axis solar tracker. The analysis is based on solar insolation data for 2019 obtained from the research team’s HP-2000 weather station. Dual-axis solar tracker and single-axis vertical trackers are shown to have the best year-round generation, providing an increase of 13.2% and 11.5%, respectively, against the static PV cells (no change in tilt).
Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation

Quality Estimation (QE) plays an essential role in applications of Machine Translation (MT).Traditionally, a QE system accepts the original source text and translation from a black-box MT system as input.Recently, a few studies indicate that as a by-product of translation, QE benefits from the model and training data's information of the MT system where the translations come from, and it is called the "glass-box QE".In this paper, we extend the definition of "glass-box QE" generally to uncertainty quantification with both "blackbox" and "glass-box" approaches and design several features deduced from them to blaze a new trial in improving QE's performance.We propose a framework to fuse the feature engineering of uncertainty quantification into a pretrained cross-lingual language model to predict the translation quality.Experiment results show that our method achieves state-of-the-art performances on the datasets of WMT 2020 QE shared task.
Search For Deep Graph Neural Networks

Current GNN-oriented NAS methods focus on the search for different layer aggregate components with shallow and simple architectures, which are limited by the 'over-smooth' problem. To further explore the benefits from structural diversity and depth of GNN architectures, we propose a GNN generation pipeline with a novel two-stage search space, which aims at automatically generating high-performance while transferable deep GNN models in a block-wise manner. Meanwhile, to alleviate the 'over-smooth' problem, we incorporate multiple flexible residual connection in our search space and apply identity mapping in the basic GNN layers. For the search algorithm, we use deep-q-learning with epsilon-greedy exploration strategy and reward reshaping. Extensive experiments on real-world datasets show that our generated GNN models outperforms existing manually designed and NAS-based ones.
DISCOURSES OF VICTIMHOOD AND IDENTITY POLITICS ON SOCIAL MEDIA: UNDERSTANDING AFFECTIVE POLARIZATION DURING THE US ELECTION

This cross-platform digital ethnography examines the nuances of how emotions are expressed and who they are directed towards within social media in order to better understand the phenomenon of affective polarization and the increased emotionality online. As part of a larger three-year SSHRC-funded comparative study between the US and Canadian elections, the focused dataset for this project draws on grounded theory (Charmaz, 2006) and our exploration of 1800 social media posts from the political left and right across social media platforms: Twitter, Facebook, and Gab. By examining how social media users discursively construct representations of self and other through expressions of us/them dichotomies, this project seeks to better understand polarized political identities and how social media users emphasize that their morals and values are similar or distinct from others. How do people on the left and the right feel victimized by the other? What are the moral and emotional injuries as well as the identity politics upon which they base their claims to victimhood and simultaneously place blame on the other? How do social media users rhetorically express their indignation through us/them dichotomizing, to justify their negative affect as well as enactments of revenge as moral duty? In addition to presenting key findings, this talk highlights our innovative approach to affective discourse analysis developed over the past two years of iterative, grounded theoretical qualitative study.
Dataset Creation Framework for Personalized Type-Based Facet Ranking Tasks Evaluation

Faceted Search Systems (FSS) have gained prominence in many existing vertical search systems. They provide facets to assist users in allocating their desired search target quickly. In this paper, we present a framework to generate datasets appropriate for simulation-based evaluation of these systems. We focus on the task of personalized type-based facet ranking. Type-based facets (t-facets) represent the categories of the resources being searched in the FSS. They are usually organized in a large multilevel taxonomy. Personalized t-facet ranking methods aim at identifying and ranking the parts of the taxonomy which reflects query relevance as well as user interests. While evaluation protocols have been developed for facet ranking, the problem of personalising the facet rank based on user profiles has lagged behind due to the lack of appropriate datasets. To fill this gap, this paper introduces a framework to reuse and customise existing real-life data collections. The framework outlines the eligibility criteria and the data structure requirements needed for this task. It also details the process to transform the data into a ground-truth dataset. We apply this framework to two existing data collections in the domain of Point-of-Interest (POI) suggestion. The generated datasets are analysed with respect to the taxonomy richness (variety of types) and user profile diversity and length. In order to experiment with the generated datasets, we combine this framework with a widely adopted simulated user-facet interaction model to evaluate a number of existing personalized t-facet ranking baselines.
Design and Practice of the Information Teaching Mode under the Background of Vocational Education -- MEE Model

Based on the background of vocational education and the theory of the ARCS model, this study explores the Motivation-Enhancing Experience Model (MEE Model) which can improve the learning motivation of vocational students combined with information technology and mobile social software as tools, in order to solve the problems of the learning burnout in vocational college. Considering the responsibility of vocational education being to improve skills, the research constructs a model, and clarifies the specific implementation methods and key points. This model uses mobile games, audio and video recording and other information-based means for experience design, applies social software as the carrier to put informationized portfolio evaluation into practice, both of which effectively integrate information technology into vocational education reform. This model has strong pertinence to vocational education and can be widely transferred and applied.
ACCOMMODATING COMMUNICATION WITH CONVERSATIONAL AGENTS: EXAMINING THE PERCEPTIONS AND BEHAVIORS OF OLDER ADULTS WHEN USING VOICE ASSISTANT TECHNOLOGY

The purpose of this study is to examine the communicative relationship between older adults and conversational agents (CA), such as a Google Home Mini, to understand if and how interaction with AI-based voice technology affects perceptions, technological adoption, and, ultimately, human-machine communicative behaviors. Using the Communication Accommodation Theory (CAT) framework (Gallois &amp; Giles, 2015), and the categorical schema as outlined in the Unified Theory of Adoption and Utilization of Technology (UTAUT) model (Venkatesh et al., 2003) of technology acceptance, we qualitatively assess the relationship between expectations for use and ongoing / post-interaction user attitudes. CAT focuses on the adjustments we make in our perceptions of and engagement in communicative behaviors. In other words, we enter into communicative situations with intentions and motivations derived from antecedent socio-historical context in mind. This squares with what the UTAUT model details as influencers of technological adoption and use: performance expectancy, effort expectancy, social influence, and facilitating conditions (Venkatesh et al., 2003). We use these constructs as a coding guideline to index data scraped from the Mini, and collected from surveys, interview transcripts, user journals, and field notes throughout a 10-week study. Historically, CAT is applied to human-human communication exchanges. As the theory posits that interpersonal relationships can and will influence motivations or intentions for dyadic communication, this makes sense. However, we argue that as AI-based voice technologies become more sophisticated as voice assistants enter our intimate spaces, the application of CAT to the human-machine communicative relationship is warranted.
Blockchain IoT Concepts for Smart Cities

In the last few years, incredible work has been done in the field of smart cities. In 2016, the Indian government launched the smart city mission to develop cities by using smart solutions to problems. The main objective of advancing smart cities is to improve the quality of life factors for people living in them. Smart cities are focusing on the use of modern resources to create a more reliable urban environment using optimal resources. There are technologies like cloud computing and Internet of Things (IoT) that have been processed to achieve that objective. Blockchain technology is also one of the promising technologies that can provide valuable services to its user. Blockchain technology can provide solutions to a wide variety of problems that are commonly encountered by smart cities. But practically, the implementation of this technique will depend on the government and society. This chapter integrates blockchain technology with smart IoT devices to provide a complete structure of a smart city. It concentrates on the adoption of a blockchain technology-based solution and its utilization in smart city advancement. The concept of this chapter is to provide a comprehensive analysis of smart cities based on blockchain technology and smart IoT devices.
Quality, Security Issues, and Challenges in Multi-cloud Environment: A Comprehensive Review

Nowadays, cloud computing is popular for various reasons such as increased productivity, speed, efficiency, performance, security, and cost savings. These advantages are achieved because instead of owning their own computing infrastructure, companies can rent a wide range of services like data storage, databases, servers, networking, and software from a cloud service provider. A multi-cloud sends more than one public cloud from various cloud suppliers. A multi-cloud sending can utilize numerous Infrastructure-as-a-Service (IaaS) merchants, or it could utilize an alternate seller for IaaS, Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS) administrations. On the off chance that the server farms are exceptionally far separated topographically, it will be hard to adjust loads across various clouds which likewise build the idleness of information conveyance. There also exists possibility of greater challenges for service quality, security, and privacy attacks. This chapter presents an attempt to elaborate various algorithms used for the security of data and to provide quality service.
HeMoG: A White-Box Model to Unveil the Connection between Saliency Information and Human Head Motion in Virtual Reality

Immersive environments such as Virtual Reality (VR) are now a main area of interactive digital entertainment. The challenge to design personalized interactive VR systems is specifically to guide and adapt to the user's attention. Understanding the connection between the visual content and the human attentional process is therefore key. In this article, we investigate this connection by first proposing a new head motion predictor named HeMoG. HeMoG is a white-box model built on physics of rotational motion and gravitation. Second, we compare HeMoG with existing reference Deep Learning models. We show that HeMoG can achieve similar or better performance and provides insights on the inner workings of these black-box models. Third, we study HeMoG parameters in terms of video categories and prediction horizons to gain knowledge on the connection between visual saliency and the head motion process.
Lower- versus higher-order nonclassicalities for a coherent superposed quantum state

A coherent state is defined conventionally in different ways such as a displaced vacuum state, an eigenket of an annihilation operator, or as an infinite dimensional Poissonian superposition of Fock states. In this work, we describe a superposition <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mo stretchy="false">(</mml:mo> <mml:mi>t</mml:mi> <mml:mi>a</mml:mi> <mml:mo>+</mml:mo> <mml:mi>r</mml:mi> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msup> <mml:mi>a</mml:mi> <mml:mo>†<!-- † --></mml:mo> </mml:msup> </mml:mrow> <mml:mo stretchy="false">)</mml:mo> </mml:math> of field annihilation and creation operators acting on a continuous variable coherent state <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo stretchy="false">|</mml:mo> </mml:mrow> <mml:mi>α<!-- α --></mml:mi> <mml:mo fence="false" stretchy="false">⟩<!-- ⟩ --></mml:mo> </mml:math> and specify it by <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo stretchy="false">|</mml:mo> </mml:mrow> <mml:mi>ψ<!-- ψ --></mml:mi> <mml:mo fence="false" stretchy="false">⟩<!-- ⟩ --></mml:mo> </mml:math> . We analyze the lower- as well as higher-order nonclassical properties of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo stretchy="false">|</mml:mo> </mml:mrow> <mml:mi>ψ<!-- ψ --></mml:mi> <mml:mo fence="false" stretchy="false">⟩<!-- ⟩ --></mml:mo> </mml:math> . The comparison is performed by using a set of nonclassicality witnesses (e.g., higher-order photon statistics, higher-order antibunching, higher-order sub-Poissonian statistics, higher-order squeezing, Agarwal–Tara parameter, Klyshko’s condition, and a relatively new concept, matrix of phase-space distribution). It is found that the higher-order criteria are much more efficient to detect the presence of nonclassicality as compared to lower-order conditions except squeezing, where the lower-order one is more competent than its higher-order counterpart.
DEVELOPING A GOOD QUALITY CHATBOT

Chatbots are commonly known nowadays as intelligent personal assistants which are designated to accomplish given tasks. The development of a good chatbot depends on the elements and features which then will be reflected in the performance and functionality of the application, user’s experience as well as satisfaction in order to complete their task. Two methodologies are applied in this paper, which are preliminary study and literature survey. The outcomes were reflected into the development of a new chatbot based on the attributes gained from the observation. The aim of this paper is to investigate the possible quality attributes from past researches and to develop a platform that will subsequently be used to build a quality chatbot for the tourism domain.
Interpolativity and Continuity of Similarity-Based Reasoning Fuzzy Inference

Similarity-based reasoning is one of the well-established inference mechanisms which is widespread in the literature.An inference mechanism is required to have some properties to be an acceptable inference mechanism that can be used in an application.Interpolativity and continuity are two of the major controlling properties of an inference mechanism.In this work, we represent the SBR inference mechanism as a function between two spaces of fuzzy sets following which we ascertain the conditions on different operations involved in a similarity-based reasoning inference mechanism that satisfies the above-mentioned properties.
Cybersecurity for next generation healthcare in Qatar

Background: IoMT (Internet of Medical Things) devices (often referred to IoMT domain) have the potential to quickly diagnose and monitor patients outside the hospital by transmitting information through the cloud domain using wireless communication to remotely located medical professionals (user domain). Figure 1 shows the proposed IoMT framework designed to improve the privacy and security of the healthcare infrastructure. Methods: The framework consists of four modules: 1. Intrusion Detection System (IDS)1 using deep learning (DL) to identify bluetooth-based Denial-of-Service (DoS)-attacks on IoMT devices and is deployed on edge-computing to secure communication between IoMT and edge. 2. IDS1 is backed up with identity-based cryptography to encrypt the data and communication path. 3. Besides the identity-management system (to authenticate users), it is modeled with aliveness detection using face authentication techniques at the edge to guarantee the confidentiality, integrity, and availability (CIA) of the framework. 4. At the cloud level, another IDS2 using MUSE (Merged-Hierarchical-Deep-Learning-System-with-Layer-Reuse) is proposed to protect the system against Man-In-The-Middle attacks, while the data is transferred between IoMT-EDGE-CLOUD. Results: These four modules are developed independently by precisely analyzing dependencies. The performance of IDS3 in terms of precision is 99% and for the identity-management system, the time required to encrypt and decrypt 256-bit key is 66 milliseconds and 220 milliseconds respectively. The true positive rate is 90.1%, which suggests real-time detection and authentication rate. IDS (2) using MUSE (12-layer) the accuracy is &gt;95%, and it consumes 15.7% to 27.63% less time to train than the smaller four-layer model. Conclusion: Our designed models suit edge devices and cloud-based cybersecurity systems and support the fast diagnosis and care required by critically ill patients in the community.
Stacking Based Ensemble Learning for Improved Software Defect Prediction

The process of developing a good quality software requires rigorous testing of the software modules. The effort and resources required for testing can be reduced by early prediction of the defects present in various modules of the software. This paper aims to do a comparative research on different classification algorithms taking into consideration the data imbalance and high dimension of the defect datasets. Artificial Neural Network (ANN), Decision Trees, K-nearest neighbour, SVM and Ensemble Learning are some of the algorithms in machine learning that have been used for classifying the modules in software as defect-prone and not defect-prone. For datasets from PROMISE repository, multiple software metrics have been evaluated with feature selection (FS) techniques such as Recursive Feature Elimination (RFE) and correlation based FS combined with Synthetic Minority Oversampling Technique for imbalanced datasets. In this research work, Stacking Ensemble technique gave best results for all the datasets with defect prediction accuracy more than 0.9 among the algorithms used for this experiment.