Reviewerly/exHarmony huggingface dataset:
Toward a correct and optimal time-aware cloud resource allocation to business processes

Cloud is an increasingly popular computing paradigm that provides on-demand services to organizations for deploying their business processes over the Internet as it reduces their needs to plan ahead for provisioning resources. Cloud providers offer competitive pricing strategies (e.g., on-demand, reserved, and spot) specified based on temporal constraints to accommodate organizations’ changing and last-minute demands. Despite their varieties and benefits to optimize business process deployment cost, using those pricing strategies can lead to violating time constraints and exceeding budget constraints due to inappropriate decisions when allocating cloud resources to business processes. In this paper, we present an approach to guarantee a correct and optimal time-aware allocation of cloud resources to business processes. Correct because time constraints on these processes are not violated. And, optimal because the deployment cost of these processes is minimized. For this purpose, our approach uses timed automata to formally verify the matching between business processes’ temporal constraints and cloud resources’ time availabilities and linear programming to optimize deployment costs. Experiments demonstrate the technical doability of our proposed approach.
Robustness for Non-Parametric Classification: A Generic Attack and Defense

Adversarially robust machine learning has received much recent attention. However, prior attacks and defenses for non-parametric classifiers have been developed in an ad-hoc or classifier-specific basis. In this work, we take a holistic look at adversarial examples for non-parametric classifiers, including nearest neighbors, decision trees, and random forests. We provide a general defense method, adversarial pruning, that works by preprocessing the dataset to become well-separated. To test our defense, we provide a novel attack that applies to a wide range of non-parametric classifiers. Theoretically, we derive an optimally robust classifier, which is analogous to the Bayes Optimal. We show that adversarial pruning can be viewed as a finite sample approximation to this optimal classifier. We empirically show that our defense and attack are either better than or competitive with prior work on non-parametric classifiers. Overall, our results provide a strong and broadly-applicable baseline for future work on robust non-parametrics. Code available at this https URL .
Teachers’ Perceptions on Using Augmented Reality for Language Learning in Primary Years Programme (PYP) Education

Augmented Reality (AR) is a relatively new technology and is increasingly being posited as an educational game-changer, particularly - but not exclusively - in language learning. On a scale where the virtual and real world sit at opposite ends of the spectrum, AR sits in-between, combining real world elements with virtual overlays. Research suggests that an obstacle to English language learning in countries where English is not the first language (EFL) is a lack of authentic situations outside of the classroom for practising English communication skills. AR technology, by combining elements of real-life and virtual content could potentially overcome some of these limitations. However, as with all technologies, the use of AR in the classroom is not only confined to the learner, but also largely dependent on the willingness and skills of teachers to utilise it effectively. This study hopes to shed some light on the possible uses, benefits, and challenges that AR may present to the field of EFL learning in an Egyptian International Baccalaureate (IB) school.
Algorithm Selection for Optimal Multi-Agent Pathfinding

The challenge of finding an optimal solution to a multi-agent path finding (MAPF) problem has attracted significant academic and industrial interest in recent years. While the problem is NP-Hard, modern optimal MAPF algorithms can scale to solve problems with hundreds of agents. Nevertheless, no single optimal MAPF algorithm dominates all benchmarks problems, and there are no clear, provable, guidelines for when each algorithm should be used. To address this, we present the first successful Algorithm Selection (AS) model for optimal MAPF. We propose two approaches for learning an AS model. The first approach uses a standard supervised learning algorithm with a set of handcrafted MAPF-specific features. The second approach, casts a MAPF problem to an image and applies a deep Convolutional Neural Network to classify it. We evaluate both approaches over a large dataset and show that using an AS model to select which algorithm to use for each instance results in solving more problems and in a shorter runtime compared to the state of the art.
Leveraging Principal Parts for Morphological Inflection

This paper presents the submission by the CU Ling team from the University of Colorado to SIGMORPHON 2020 shared task 0 on morphological inflection.The task is to generate the target inflected word form given a lemma form and a target morphosyntactic description.Our system uses the Transformer architecture.Our overall approach is to treat the morphological inflection task as a paradigm cell filling problem and to design the system to leverage principal parts information indirectly for better morphological inflection when the training data is limited.We train one model for each language separately without external data.The overall average performance of our submission ranks the first in both average accuracy and Levenshtein distance from the gold inflection among all submissions including those using external resources.
Bi-Directional Dense Traffic Counting Based on Spatio-Temporal Counting Feature and Counting-LSTM Network

Machine vision based vehicle counting and traffic flow estimation are challenging problems especially for dense traffic scenarios. Previous <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">line of interest</i> (LOI) counting methods rarely focus on dense scenarios and their performance largely relies on the accuracy of tracking. Avoiding the use of complex tracking methods, an LOI counting framework is proposed to address the bi-directional LOI counting problem in dense scenarios. There are three main contributions. Firstly, instead of treating the LOI vehicle counting problem as a combination of detecting and tracking of individual vehicles, the bi-directional traffic flow is taken as a whole and a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spatio-temporal counting feature</i> (STCF) is proposed for extracting bi-directional traffic flow features in dense traffic scenarios. Secondly, without relying on a multi-target tracking process for tracking and counting each vehicle, a counting network is proposed, called the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">counting Long Short-Term Memory</i> (cLSTM) network, to do analysis of the bi-directional STCF features and vehicle counting in successive video frames. Lastly, an estimation model is designed for estimating traffic flow parameters including speed, volume and density. Experiments performed on the UA-DETRAC dataset and the captured videos show that the proposed vehicle counting method outperforms the tested representative LOI counting methods in both accuracy and speed, and that the proposed framework can efficiently estimate traffic flow parameters including speed, volume and density in real time.
Patient-Driven Diabetes Technologies: Sentiment and Personas of the #WeAreNotWaiting and #OpenAPS Movements

Background: Patients with diabetes have developed innovative do-it-yourself (DIY) methods for adapting existing medical devices to better fit individual needs. Method: A multiple method study used Symplur Analytics to analyze aggregated Twitter data of #WeAreNotWaiting and #OpenAPS tweets between 2014 and 2017 to examine DIY patient-led innovation. Conversation sentiment was examined between diabetes stakeholders to determine changes over time. Two hundred of the most shared photos were analyzed to understand visual representations of DIY patient-led innovations. Finally, discourse analysis was used to identify the personas who engage in DIY patient-led diabetes technologies activities and conversations on Twitter. Results: A total of 7886 participants who generated 46 578 tweets were included. Sentiment analysis showed that 82%-85% of interactions around DIY patient-led innovation was positive among patient/caregiver and physician groups. Through photo analysis, five content themes emerged: (1) disseminating media and conference coverage, (2) showcasing devices, (3) celebrating connections, (4) providing instructions, and (5) celebrating accomplishments. Six personas emerged across the overlapping userbase: (1) fearless leaders, (2) loopers living it up, (3) parents on a mission, (4) the tech titans, (5) movement supporters, and (6) healthcare provider advocates. Personas had varying goals and behaviors within the community. Conclusions: #WeAreNotWaiting and #OpenAPS on Twitter reveal a fast-moving patient-led movement focused on DIY patient innovation that is further mobilized by an expanding and diverse userbase. Further research is indicated to bring technology savvy persons with diabetes into conversation with healthcare providers and researchers alike.
The Impact of Managerial Decision-Making on Employee Motivation in Manufacturing Companies

In terms of competition, manufacturing companies are focusing more and more on employee performance as well as motivation, the latter of which seems to be a prerequisite for the long-term performance of employees and thus the sustainable success of the company.Managerial decision-making, another phenomenon gaining scientific attention, can be considered a challenging function and the most important activity of a manager.But what is the impact of managerial decision-making on the motivation of employees?Can managerial decision-making influence employee motivation?The aim of this paper is to investigate the impact of managerial decision-making on the motivation of employees in manufacturing companies.As the main method of data acquisition, a questionnaire survey among employees was conducted.For further processing and evaluation of the survey results, a contingency table and Chi-square test were used, followed by Student's T-test and F-test for calculating testability and verifying hypotheses.The results brought answers to the questions regarding the relationship between managerial decision-making and motivation of employees in the selected production companies.Factors and benefits affecting the motivation of employees and leading to their higher work performance was also determined, as well as to what extent a manager is able to influence performance.
Role of Mobile Communication with Emerging Technology in COVID’19

This study shows impact of technology for communication in our day to day life and how our system welcomed emerging technology during this pandemic.Corona virus is causing serious health hazards all over the world causing many deaths and infections.It's not an ordinary disease, as it spread very easily due to number of factors like handshake or touching infectious surface and its complete treatment has also became a challenge for doctors as vaccine is not there and proper equipment are also not there because number of COVID -19 patients are very large.As its further spread is nearly impossible to stop but we can control the spread of infection by getting least socialized.Government of many countries all over the world has announced complete lockdown ordering all institutes, shopping malls, transports and organization to be closed so that social gathering could be avoided as far as possible, in this case our mobile phones are becoming our service provider.Mobile phone and its applications has become the life savior indirectly as whatever we used to do before, is possible to do now also like banking, ordering groceries, video calls, news, job, education and many more.Our technologies like artificial intelligence, big data analysis, robotics, drone, contact tracking, 5G, 3D printers etc., are helping to analyze and control the spread also making the treatment fast as well as effective.This study also focusing on connection with 5G communication and how this is helping our life along with mobile computing.
An Overview of Deep Learning in Industry

This chapter introduces the reader to many important applications of deep learning in industry. It provides a high-level overview of deep learning and its key architectures. The chapter provides a survey and discussion of the main applications of deep learning, categorized into four general tasks: recognition; generation; decision-making; and forecasting. Deep learning models are artificial neural networks which emphasize the use of multiple connected layers to gradually transform input signals to the desired outputs. Generating new content is an important area of machine learning with applications ranging from conversational artificial intelligence to knowledge discovery, and generative models are approaches that simulate how the data from the desired distribution are generated. Deep learning approaches, and in particular Convolutional neural networks, are especially well suited for processing visual data. The chapter concludes with a discussion on the strengths and weakness, as well as the future applications of deep learning.
Effects of Uncertainty Visualization on Map-Based Decision Making Under Time Pressure

Most of our daily activities in a highly mobile digital society require timely spatial decision making. Much of such decision-making is supported by map displays on various devices with different modalities. Spatial information visualized on maps, however, is always subject to a multitude of uncertainties. If space-time decision-makers are not informed about potential uncertainties, misleading, or at worst, life-threatening outcomes might result from map-based decisions. Therefore, data uncertainties should be communicated to decision-makers, especially when they are made with limited time resources and when decision outcomes can have dramatic consequences. Thus, the current study investigates how data uncertainty visualized in maps might influence the process and outcomes of spatial decision making, especially when made under time pressure in risky situations. Although there is very little empirical evidence from prior uncertainty visualization research that considered decision time constraints, we hypothesized that uncertainty visualization would also have an effect on decision-making under time critical and complex decision contexts. Using a map-based helicopter landing scenario in mountainous terrain, we found that neither time pressure or uncertainty affected participants decision-making accuracy. However, uncertainty affected participants' decision strategies, and time pressure affected participants' response times. Specifically, when presented with two equally correct answers, participants avoided uncertainty more often at a cost of landing distance (an equally important decision criteria). We interpret our results as consistent with a loss-aversion heuristic and suggest implications for the study of decision-making with uncertainty visualizations.
Feature Pyramid Attention Model and Multi-Label Focal Loss for Pedestrian Attribute Recognition

At present, there are many challenges in the field of pedestrian attribute recognition, such as small targets of some attributes, imbalanced samples, and low recognition accuracy of complex samples.In view of the above problems, we improved the model in two perspectives: 1) We proposed Feature Pyramid Attention Model (FPAM).In order to solve the problem that attributes are distributed in different locations in the pedestrian image, FPAM adopted the attention mechanism on the basis of ResNet-50, by which the model's attention could be focused on key areas of the image.As for the difficulty in small targets attributes, we adopted feature pyramid integration strategy; 2) We proposed Multi Label Focal Loss (MLFL).Referring to Binary Cross Entropy Loss Function (CE) and Weight Binary Cross Entropy Loss Function (WCE), we added the weight parameters of samples which are difficult to classify to improve the recognition accuracy, and the rate of convergence was increased.Results show that our proposed method achieves 84.83% mA, 79.37% Accuracy, 87.47% Precision, 86.09% Recall, and 86.77% F1 on PETA dataset.
VIRTUAL REALITY TO SUPPORT TECHNICAL KNOWLEDGE IN CULTURAL HERITAGE. THE CASE STUDY OF CRYPTOPORTICUS IN THE ARCHAEOLOGICAL SITE OF EGNATIA (ITALY)

Abstract. Digitalization and interactivity of reality in Augmented and Virtual Environments represent the synergic union between current technology potentialities and smartness of users in going beyond the traditional perception of real environment. As it is well-known, touristic bodies already taken advantages of Virtual Environments as cultural and touristic promotion of historic and archaeological sites. However, the analysis of potentialities in supporting technical community and professionals are still underway. Starting from the survey of instruments and protocols in previous experiences, the work presents a methodological flow aimed to create a virtual environment using 360° images – Virtual Tour – where the first phase of technical knowledge in historic sites can be resumed. In detail, a double level of knowledge can be reached: firstly, a virtual environment containing information about the actual state of conservation, then an upgraded one with historic and technical information (e.g. reports, images, surveys, etc.) added in an interactive as well intuitive way. The protocol has been applied to the undergrounded Cryptoporticus of Egnatia, an archaeological site in Apulia Region (Italy).
Exact and Practical Pattern Matching for Quantum Circuit Optimization

Quantum computations are typically performed as a sequence of basic operations, called quantum gates. Different gate sequences, called quantum circuits, can implement the same overall quantum computation. Since every additional quantum gate takes time and introduces noise into the system, it is important to find the smallest possible quantum circuit that implements a given computation, especially for near-term quantum devices that can execute only a limited number of quantum gates before noise renders the computation useless. An important building block for many quantum circuit optimization techniques is pattern matching: given a large and small quantum circuit, we would like to find all maximal matches of the small circuit, called a pattern , in the large circuit, considering pairwise commutation of quantum gates. In this work, we present the first classical algorithm for pattern matching that provably finds all maximal matches and is efficient enough to be practical for circuit sizes typical for near-term devices. We demonstrate numerically 1 that combining our algorithm with known pattern-matching-based circuit optimization techniques reduces the gate count of a random quantum circuit by ∼ 30% and can further improve practically relevant quantum circuits that were already optimized with state-of-the-art techniques.
A Transformer Approach to Contextual Sarcasm Detection in Twitter

Understanding tone in Twitter posts will be increasingly important as more and more communication moves online.One of the most difficult, yet important tones to detect is sarcasm.In the past, LSTM and transformer architecture models have been used to tackle this problem.We attempt to expand upon this research, implementing LSTM, GRU, and transformer models, and exploring new methods to classify sarcasm in Twitter posts.Among these, the most successful were transformer models, most notably BERT.While we attempted a few other models described in this paper, our most successful model was an ensemble of transformer models including BERT, RoBERTa, XLNet, RoBERTa-large, and ALBERT.This research was performed in conjunction with the sarcasm detection shared task section in the Second Workshop on Figurative Language Processing, co-located with ACL 2020.
A Technical and Conceptual Framework for Serious Role-Playing Games in the Area of Social Skill Training

Virtual role-playing games can provide an authentic experience of situated learning and allow for trying out different problem-solving and communication strategies without consequences in the real world. This is of particular interest and benefit for the training of social skills. This article presents a conceptual and technical framework for serious role-playing games for the training of specific social skills in virtual 2D learning environments involving chatbots in dialog-centric settings. It summarizes different use cases and evaluation results from prior studies. From the design perspective, several distinctive conceptual features characterize our framework: (1) chat-like interaction with an AI-controlled chatbot, (2) separate phases of immersion and reflection to facilitate a change of perspective that is considered conducive for learning, (3) the learning process is emphasized by means of adaptive feedback based on individual analyses. We propose a system architecture that is based on three components: (1) AI-controlled chatbots that adapt to the player’s behavior, (2) a multi-agent blackboard system as the backbone in order to keep components independent and optimize performance due to parallel processing, and (3) intelligent support for an automated evaluation of the player’s performance and feedback generation. The training scenarios presented and discussed in this article include workplace-oriented conflict management, patient-centered medical interviews, and customer complaint management. First evaluation studies indicate that the scenarios may be well-suited for real training situations. Due to its flexible architecture, our framework and approach can easily be tailored to different settings and use cases and thus serve as a basis for future research focusing on the adaptation to other contexts and systems. On the basis of these developments, we elaborate important design dimensions, reflect and discuss general issues and major challenges, summarize and contrast different approaches and strategies, as well as identify opportunities for serious role-playing games in the area of social skills training.
Gradient descent follows the regularization path for general losses.

Recent work across many machine learning disciplines has highlighted that standard descent methods, even without explicit regularization, do not merely minimize the training error, but also exhibit an implicit bias. This bias is typically towards a certain regularized solution, and relies upon the details of the learning process, for instance the use of the cross-entropy loss. In this work, we show that for empirical risk minimization over linear predictors with arbitrary convex, strictly decreasing losses, if the risk does not attain its infimum, then the gradient-descent path and the algorithm-independent regularization path converge to the same direction (whenever either converges to a direction). Using this result, we provide a justification for the widely-used exponentially-tailed losses (such as the exponential loss or the logistic loss): while this convergence to a direction for exponentially-tailed losses is necessarily to the maximum-margin direction, other losses such as polynomially-tailed losses may induce convergence to a direction with a poor margin.