Reviewerly/exHarmony huggingface dataset:
A Multi-Size Neural Network with Attention Mechanism for Answer Selection

Semantic matching is of central significance to the answer selection task which aims to select correct answers for a given question from a candidate answer pool. A useful method is to employ neural networks with attention to generate sentences representations in a way that information from pair sentences can mutually influence the computation of representations. In this work, an effective architecture,multi-size neural network with attention mechanism (AM-MSNN),is introduced into the answer selection task. This architecture captures more levels of language granularities in parallel, because of the various sizes of filters comparing with single-layer CNN and multi-layer CNNs. Meanwhile it extends the sentence representations by attention mechanism, thus containing more information for different types of questions. The empirical study on three various benchmark tasks of answer selection demonstrates the efficacy of the proposed model in all the benchmarks and its superiority over competitors. The experimental results show that (1) multi-size neural network (MSNN) is a more useful method to capture abstract features on different levels of granularities than single/multi-layer CNNs; (2) the attention mechanism (AM) is a better strategy to derive more informative representations; (3) AM-MSNN is a better architecture for the answer selection task for the moment.
Towards Accurate Text-based Image Captioning with Content Diversity Exploration

Text-based image captioning (TextCap) which aims to read and reason images with texts is crucial for a machine to understand a detailed and complex scene environment, considering that texts are omnipresent in daily life. This task, however, is very challenging because an image often contains complex texts and visual information that is hard to be described comprehensively. Existing methods attempt to extend the traditional image captioning methods to solve this task, which focus on describing the overall scene of images by one global caption. This is infeasible because the complex text and visual information cannot be described well within one caption. To resolve this difficulty, we seek to generate multiple captions that accurately describe different parts of an image in detail. To achieve this purpose, there are three key challenges: 1) it is hard to decide which parts of the texts of images to copy or paraphrase; 2) it is non-trivial to capture the complex relationship between diverse texts in an image; 3) how to generate multiple captions with diverse content is still an open problem. To conquer these, we propose a novel Anchor-Captioner method. Specifically, we first find the important tokens which are supposed to be paid more attention to and consider them as anchors. Then, for each chosen anchor, we group its relevant texts to construct the corresponding anchor-centred graph (ACG). Last, based on different ACGs, we conduct multi-view caption generation to improve the content diversity of generated captions. Experimental results show that our method not only achieves SOTA performance but also generates diverse captions to describe images.
한·일 대학생의 문화간 의사소통능력, 영어 학습동기 및 영어 매체사용 비교 연구

The purpose of this study was to compare Korean university students with Japanese counterparts in terms of their intercultural communicative competence, their English learning motivation, and their use of English media. For the study, 89 Korean and 70 Japanese college students participated in a survey specifically to find out similarities and differences regarding the above factors, which have been shown to be related to each other in their overall intercultural communication. Besides, the study sought to explore whether variables such as the participants’ majors, English proficiency, and experiences living in different countries would have any effects on the four factors measured in the questionnaire. Based on the statistical analyses, Korean college students showed a higher intercultural communicative competence, and learning motivation than those of their Japanese counterparts. Based on the findings, pedagogical implications are discussed.
ENGLISH CORPORA RESEARCH: THE MEANING OF CREATIVE AND WRITING CONTEXTS IN IWEB APLICATION

Corpus linguistics learns about language with the help of modern computer technology in language data collection. language corpora are one of the important aspects related to langaunge corpora is a matter of copyright, especially if the findings from the corpus will be disseminated through handouts or published in any form.The research explored the field of computational linguistics to find the meaning of Creative Writing Context in online learning during pandemic. Corpus-based research is also considered as a broad approach to qualitative research or as a method for gathering qualitative data. more than 1 million sentences that connect creative words with writing. Broadly speaking, if we are looking for creative words, it will be related to writing.
Behavioral Neural Networks

We provide an axiomatic foundation for a class of neural-network models applied to decision-making under risk, called neural-network expected utility (NEU) models. Motivated by classic experimental findings, we weaken the independence axiom in a novel way. We show how to use simple neurons, referred to as behavioral neurons, in a NEU model to capture behavioral biases such as reference dependence and the certainty effect. Empirically, we find that focusing on NEU models with multiple types of behavioral neurons helps mitigate overfitting, and some simple NEU models with natural interpretation have good predictive power.
Arrendale Library: Permalinks for Online Services: EBSCO Services

This guide will show how to obtain permalinks to journal articles, ebooks and ebook chapters, and videos found in the Library's online research services. Permalinks can be used to create links to learning objects in Canvas courses.
File-level identification support in Dataverse and DataverseNO

Presentation at the online workshop EOSC-Nordic FAIRification Webinar: FAIRification STEP 2 - FAIR principle F3 ( https://indico.neic.no/event/171/ , arranged by EOSC-Nordic ( https://www.eosc-nordic.eu/ 03.02.2021.
Click-Through Rate Prediction Using Graph Neural Networks and Online Learning

Recommendation systems have been extensively studied by many literature in the past and are ubiquitous in online advertisement, shopping industry/e-commerce, query suggestions in search engines, and friend recommendation in social networks. Moreover, restaurant/music/product/movie/news/app recommendations are only a few of the applications of a recommender system. A small percent improvement on the CTR prediction accuracy has been mentioned to add millions of dollars of revenue to the advertisement industry. Click-Through-Rate (CTR) prediction is a special version of recommender system in which the goal is predicting whether or not a user is going to click on a recommended item. A content-based recommendation approach takes into account the past history of the user's behavior, i.e. the recommended products and the users reaction to them. So, a personalized model that recommends the right item to the right user at the right time is the key to building such a model. On the other hand, the so-called collaborative filtering approach incorporates the click history of the users who are very similar to a particular user, thereby helping the recommender to come up with a more confident prediction for that particular user by leveraging the wider knowledge of users who share their taste in a connected network of users. In this project, we are interested in building a CTR predictor using Graph Neural Networks complemented by an online learning algorithm that models such dynamic interactions. By framing the problem as a binary classification task, we have evaluated this system both on the offline models (GNN, Deep Factorization Machines) with test-AUC of 0.7417 and on the online learning model with test-AUC of 0.7585 using a sub-sampled version of Criteo public dataset consisting of 10,000 data points.
Utilizing Skipped Frames in Action Repeats via Pseudo-Actions

In many deep reinforcement learning settings, when an agent takes an action, it repeats the same action a predefined number of times without observing the states until the next action-decision point. This technique of action repetition has several merits in training the agent, but the data between action-decision points (i.e., intermediate frames) are, in effect, discarded. Since the amount of training data is inversely proportional to the interval of action repeats, they can have a negative impact on the sample efficiency of training. In this paper, we propose a simple but effective approach to alleviate to this problem by introducing the concept of pseudo-actions. The key idea of our method is making the transition between action-decision points usable as training data by considering pseudo-actions. Pseudo-actions for continuous control tasks are obtained as the average of the action sequence straddling an action-decision point. For discrete control tasks, pseudo-actions are computed from learned action embeddings. This method can be combined with any model-free reinforcement learning algorithm that involves the learning of Q-functions. We demonstrate the effectiveness of our approach on both continuous and discrete control tasks in OpenAI Gym.
Deep Learning Approach To Face Conditioning Using Invertible Conditional Generative Adversarial Networks (ICGAN)

We propose another system for evaluating generative models by means of an ill-disposed process, in which we at the same time train two models: a generative model G that catches the information conveyance, and a discriminative model D that gauges the likelihood that an example originated from the preparation information as opposed to G. The preparation strategy for G is to expand the likelihood of D committing an error. This system compares to a mini max two-player game. In the space of discretionary capacities G and D, an interesting arrangement exists, with G recuperating the preparation information conveyance and D equivalent to 1/2 all over the place. For the situation where G and D are characterized by multi-layer perceptions, the whole framework can be prepared with back propagation. There is no requirement for any Markov chains or unrolled estimated deduction systems during either preparing or age of tests. Investigations illustrate the capability of the system through subjective and quantitative assessment of the produced tests.
Multi-object Tracking with Tracked Object Bounding Box Association

The CenterTrack tracking algorithm achieves state-of-the-art tracking performance using a simple detection model and single-frame spatial offsets to localize objects and predict their associations in a single network. However, this joint detection and tracking method still suffers from high identity switches due to the inferior association method. To reduce the high number of identity switches and improve the tracking accuracy, in this paper, we propose to incorporate a simple tracked object bounding box and overlapping prediction based on the current frame onto the CenterTrack algorithm. Specifically, we propose an Intersection over Union (IOU) distance cost matrix in the association step instead of simple point displacement distance. We evaluate our proposed tracker on the MOT17 test dataset, showing that our proposed method can reduce identity switches significantly by 22.6% and obtain a notable improvement of 1.5% in IDF1 compared to the original CenterTrack's under the same tracklet lifetime. The source code is released at https://github.com/Nanyangny/CenterTrack-IOU.
3D Bioprinting in a 2D Regulatory Landscape: Gaps, Uncertainties, and Problems

In this paper we assess both the ex ante and the ex post dimensions of the regulatory landscape of 3D bioprinting governance. While the former is mainly concerned with the market approval and safety of 3D bioprinting, the latter is concerned with the matter of liability once bioprinting has been licensed or authorised for use. In this 2D landscape, we highlight three sets of choices: whether regulation should focus on the process, the product or both; whether to rely on existing regimes or create a new bespoke and distinct regulatory framework; and whether to employ top down, bottom up, or co-regulation. We identify a series of interpretative uncertainties as well as gaps in the current legal regimes. We consider an initial provisional ‘process-based’ approach and a co-regulation model for bioprinting governance. We anticipate that the study of the regulatory landscape will offer key insights in relation to other types of biofabrication.
Mixed Reality , Applications in Neurodiversity.

Technology has rendered many of the accommodations needed for ASD, possible with a recognition of Neurodiverse individuals as a minority, with a cultural context of their own. Technology renders special education obsolete, while with the accommodation technology provides, the autistic minority, the neurodiverse can lead a lifestyle no different from the neurotypical. Mixed Reality is one of those technological marvels, with wearables, creating mixed reality, both real and augmented, adding an artificial synthesia to a natural one. In this paper we consider 19 such papers, with 5 reviews proving the efficacy of mixed reality and the rest in technology of mixed reality and applications in neurodiversity accommodations and education.Keywords: neurodiversity, mixed reality, Redmond, workforce, PD, participatory design
Provable Guarantees on the Robustness of Decision Rules to Causal Interventions

Robustness of decision rules to shifts in the data-generating process is crucial to the successful deployment of decision-making systems. Such shifts can be viewed as interventions on a causal graph, which capture (possibly hypothetical) changes in the data-generating process, whether due to natural reasons or by the action of an adversary. We consider causal Bayesian networks and formally define the interventional robustness problem, a novel model-based notion of robustness for decision functions that measures worst-case performance with respect to a set of interventions that denote changes to parameters and/or causal influences. By relying on a tractable representation of Bayesian networks as arithmetic circuits, we provide efficient algorithms for computing guaranteed upper and lower bounds on the interventional robustness probabilities. Experimental results demonstrate that the methods yield useful and interpretable bounds for a range of practical networks, paving the way towards provably causally robust decision-making systems.
Estimation and Analysis of Solar Parameters in North-East India

In this paper, various solar position parameters to estimate the solar position throughout the year at individual hours have been calculated. A stepwise procedure to calculate various solar parameters like declination angle, hour angle, solar elevation, solar azimuth angles, etc. has also been given. A north eastern city of India, Agartala with latitude 23.80 N and longitude 91.50 E has been chosen as the target location. The equation of time has been calculated in this paper which plays a major role in locating the sun’s position. An algorithm has been designed to calculate the solar parameter angles that specify the sun’s position at any location, at any instant of time. The variations of the respective angles that specify the sun’s position are also given graphically for a particular hour throughout the year.
Judgments of Businesspeople Who Shake Hands in the Era of COVID-19

Traditionally, handshaking has conferred benefits for businesspeople—signaling politeness, establishing an intention to cooperate, and promoting deal-making. How might the psychological meaning of handshaking have shifted due to the COVID-19 pandemic, which led handshaking to become touted as an imminent threat to public health? Through a highly powered, preregistered study (N = 595), we compared U.S. adults’ perceptions of a businessperson who shakes hands vs. refrains from shaking hands during the era of COVID-19. Participants judged handshakers more negatively than non-handshakers, in terms of both global attitude toward them (d = 2.21) and approval of their job performance (d = 2.66). Mediation analyses suggest that businesspeople were judged negatively for shaking hands principally because doing so made them seem less competent and less moral. Judgments of handshaking were more negative among liberals than conservatives and among women than men. To maximize others’ perceptions of them, it would behoove businesspeople to refrain from shaking hands for the foreseeable future. The months and years ahead may be an unprecedented time to rediscover the psychology of handshaking and its role in the workplace.
Self-Supervised Learning for Fine-Grained Visual Categorization

Recent research in self-supervised learning (SSL) has shown its capability in learning useful semantic representations from images for classification tasks. Through our work, we study the usefulness of SSL for Fine-Grained Visual Categorization (FGVC). FGVC aims to distinguish objects of visually similar sub categories within a general category. The small inter-class, but large intra-class variations within the dataset makes it a challenging task. The limited availability of annotated labels for such a fine-grained data encourages the need for SSL, where additional supervision can boost learning without the cost of extra annotations. Our baseline achieves $86.36\%$ top-1 classification accuracy on CUB-200-2011 dataset by utilizing random crop augmentation during training and center crop augmentation during testing. In this work, we explore the usefulness of various pretext tasks, specifically, rotation, pretext invariant representation learning (PIRL), and deconstruction and construction learning (DCL) for FGVC. Rotation as an auxiliary task promotes the model to learn global features, and diverts it from focusing on the subtle details. PIRL that uses jigsaw patches attempts to focus on discriminative local regions, but struggles to accurately localize them. DCL helps in learning local discriminating features and outperforms the baseline by achieving $87.41\%$ top-1 accuracy. The deconstruction learning forces the model to focus on local object parts, while reconstruction learning helps in learning the correlation between the parts. We perform extensive experiments to reason our findings. Our code is available at https://github.com/mmaaz60/ssl_for_fgvc.
Photonic perceptron at Giga-OP/s speeds with Kerr microcombs for scalable optical neural networks

Abstract Optical artificial neural networks (ONNs) have significant potential for ultra-high computing speed and energy efficiency. We report a novel approach to ONNs that uses integrated Kerr optical micro-combs. This approach is programmable and scalable and is capable of reaching ultra-high speeds. We demonstrate the basic building block ONNs — a single neuron perceptron — by mapping synapses onto 49 wavelengths to achieve an operating speed of 11.9 x 10 9 operations per second, or Giga-OPS, at 8 bits per operation, which equates to 95.2 gigabits/s (Gbps). We test the perceptron on handwritten-digit recognition and cancer-cell detection — achieving over 90% and 85% accuracy, respectively. By scaling the perceptron to a deep learning network using off-the-shelf telecom technology we can achieve high throughput operation for matrix multiplication for real-time massive data processing.
Green Computing in Cloud Technology

According to Greenpeace, one of the major contributors of climate conditions change is the consumption of the fuel and gas, which results in atmospheric pollution such as carbon dioxide (CO2). Cloud storage is made up of data centres spread all over the world, and it uses a lot of resources to operate. Green Cloud Computing is a model that has the benefits of being environmentally sustainable, energy consuming, consolidating, virtualizing, and so on. When the use of cloud computing expands, so does its effect on the world. Collaboration between software and hardware software partners, as well as cloud providers, is necessary for green computing in cloud environments. The aim for this particular paper is given that to examine the past green computing and the cloud computing. It also providesabout cloud technology and current green computing systems that were applied to cloud platforms. In addition, related work is discussed, which offers an overview of new cloud technologies as well as green computing strategies. This article also discusses the challenges, considerations and requirements that cloud data centres and cloud organisations must understand as well as certain aspects that affect green cloud use. Finally, the proposed conceptual architecture for green computing on cloud is presented
A Behavior Annex for AADL Using the DEVS Formalism

It is essential to use modeling methods for specifying real-time and safety-critical specifications and executions as a set of computational and physical components. Thus, frameworks supporting modular, hierarchical specifications at multiple levels of abstraction for software-intensive systems are needed. Models for the structures, behaviors, and relationships benefit from separating and combining external properties and internal operations of components. Together, the Architecture Analysis and Design Language (AADL) and Discrete-Event System Specification (DEVS) provide an integrated framework where the structure and behavior designs can be systematically developed and evaluated. Needs including latency and safety analyses are supported by AADL whereas behavioral verification and validation can be supported with DEVS simulation. To create the proposed AADL-DEVS framework, a DEVS behavioral annex targeted for the DEVS-Suite simulator is developed and introduced to OSATE which supports AADL. The DEVS Annex language is detailed and exemplified using an infant incubator known as the Isolette system.
A Soft Target Threat and Risk Methodology Application

This article is focused on a Soft Target Risk application and Threat Analysis Methodology at the Faculty of Applied Informatics in Zlin. The terrorist attacks are one of the biggest security problems in the modern world. Attacks are often against Soft Targets which are places, that are typical of a high concentration of people and in the same time a low level of security. As a Soft Target we can imagine shopping centers, airport terminals, sporting arenas, airport terminals, public meetings, schools, cinemas, religious events, etc. This paper focuses of the security analysis of the Faculty of Applied Informatics (Soft Target) and its preventive safety measures.
Determinants of Firm Performance: A Subjective Model

The performance of firm is a relevant construct, in strategic management research, across the globe and frequently it is used as a dependent variable. In spite of its relevance, there is hardly any consensus about its definition, dimensionality and measurement, which limits advances in research. This review article suggests a comprehensive subjective measurement model for performance of firms, based on indicators and findings of earlier research studies. The final subjective model was developed with nine determinants/dimensions, namely, profitability performance, growth performance, market value performance of the firm, customer satisfaction, employee satisfaction, environmental audit performance, corporate governance performance and social performance. It is found that these nine dimensions or determinants cannot be used interchangeably since they represent different aspects of firm performance and different stakeholders of firms have different demands that need to be managed independently. Researchers and practitioners may use the proposed subjective model, in empirical studies, to evaluate the multiple performance of a firm.