Reviewerly/exHarmony huggingface dataset:
3D-Printed Object Identification Method using Inner Structure Patterns Configured by Slicer Software

We present an identification method for 3D-printed objects that uses differences in inner structure patterns formed by printing conditions which we can configure in slicer software. Resonant properties change depending on the shape, material, and boundary conditions of the objects, so that our method identifies objects on the basis of differences in resonant properties caused by different inner structure patterns using a machine-learning algorithm. We measured resonant properties as frequency properties using active acoustic sensing. Our method is applicable to 3D-printed objects with a low filling rate while reducing the workload of modeling the inner structure to be used as a tag. To investigate the feasibility of our method, we conducted two experimental evaluations. The results of one showed that our method can identify eight objects with an average classification accuracy of 99.3%.
Multi Agent Deep Learning with Cooperative Communication

Abstract We consider the problem of multi agents cooperating in a partially-observable environment. Agents must learn to coordinate and share relevant information to solve the tasks successfully. This article describes Asynchronous Advantage Actor-Critic with Communication (A3C2), an end-to-end differentiable approach where agents learn policies and communication protocols simultaneously. A3C2 uses a centralized learning, distributed execution paradigm, supports independent agents, dynamic team sizes, partially-observable environments, and noisy communications. We compare and show that A3C2 outperforms other state-of-the-art proposals in multiple environments.
Prominent Local Representation for Dynamic Textures Based on High-Order Gaussian-Gradients

Understanding dynamic textures (DTs) is a challenge in various computer vision applications due to the negative impacts of noise, changes of environment, illumination, and scales on capturing turbulent characteristics. In this work, we propose an efficient shallow framework for DT representation by addressing the following novel concepts. First, it is the first time in DT analysis that 2D/3D Gaussian-gradient filterings are taken into account as a pre-processing step to point out robust components against those influences in effect. Second, high-order partial derivatives of the Gaussian kernels and their informative magnitudes are exploited to forcefully capture multi-order Gaussian-gradient features. Third, these gradient kernels are investigated in multi-scale analysis of different orders and standard deviations in order to enrich more useful scale-gradient information. Finally, the obtained complementary components are shallowly encoded using a simple local operator to construct robust descriptors of High-order 2D/3D Gaussian-gradient-based Features ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathrm{HoGF}^{2D/3D}$</tex-math></inline-formula> ) against the well-known issues of DT description. Experiments for DT classification on various benchmarks have validated the interest of our approach since its performance is comparable to state-of-the-art results, including that of deep-learning methods, while it only has a small dimension.
Detection of cigarette smoke using a fiber membrane filmed with carbon nanoparticles and a fractal current law

Cigarette smoke will cause irreversible damage to human health and living environment. It is especially important to accurately detect and effectively prevent cigarette smoke. This paper fabricates a fiber membrane filmed with carbon nanopowders as a smoke sensor. The experiment reveals that the fabricated membrane is sensitive to the smoke, two laws are proposed to model the current change of the sensor. Fractal calculus is used to exactly predict the current property at the initial stage, and a fractal modification of the current law is proposed. The present technology provides an economic way to mass-production of smoke sensors.
Large Vocabulary Read Speech Corpora for Four Ethiopian Languages: Amharic, Tigrigna, Oromo, and Wolaytta

Solomon Teferra Abate, Martha Yifiru Tachbelie, Michael Melese, Hafte Abera, Tewodros Gebreselassie, Wondwossen Mulugeta, Yaregal Assabie, Million Meshesha Beyene, Solomon Atinafu, Binyam Ephrem Seyoum. Proceedings of the The Fourth Widening Natural Language Processing Workshop. 2020.
A non-specialized ensemble classifier using multi-objective optimization

Ensemble classification algorithms are often designed for data with certain properties, such as imbalanced class labels, a large number of attributes, or continuous data. While high-performing, these algorithms sacrifice performance when applied to data outside the targeted domain. We propose a non-specific ensemble classification algorithm that uses multi-objective optimization instead of relying on heuristics and fragile user-defined parameters. Only two user-defined parameters are included, with both being found to have large windows of values that produce statistically indistinguishable results, indicating the low level of expertise required from the user to achieve good results. Additionally, when given a large initial set of trained base-classifiers, we demonstrate that a multi-objective genetic algorithm aiming to optimize prediction accuracy and diversity will prefer particular types of classifiers over others. The total number of chosen classifiers is also surprisingly small – only 10.14 classifiers on average, out of an initial pool of 900. This occurs without any explicit preference for small ensembles of classifiers. Even with these small ensembles, significantly lower empirical classification error is achieved compared to the current state-of-the-art.
"Megereality": Leveraging Physical Affordances for Multi-Device Gestural Interaction in Augmented Reality

We present a novel gestural interaction strategy for multi-device interactions in augmented reality (AR), in which we leverage existing physical affordances of everyday products and spaces for intuitive interactions in AR. To explore this concept, we designed and prototyped three demo scenarios: pulling virtual sticky notes from a tablet, pulling a 3D model from a computer display, and 'slurping' color from the real-world environment to smart lights with a virtual eyedropper. By merging the boundary of digital and physical, utilizing metaphors in AR and embodying the abstract process, we demonstrate an interaction strategy that harnesses the physical affordances to assist digital interaction in AR with hand gestures.
Educommunication in digital environments: an interaction´s perspective inside and beyond the classroom

In a highly interconnected and technology-mediated world, the way in which education is recognized as a communicative phenomenon is highly relevant to understand its development and future possibilities. In this text, educommunication is analyzed from the point of view of interactions that occur in different digital learning environments, especially in ICT-enhanced classrooms, blended learning, e-learning, mobile learning and Massive Open Online Courses (MOOC). This analysis was conducted from a systematic literature review of 240 papers that describe research generated on these topics, during the last 20 years.The results show that beyond the importance of interaction for the development of educational practices, different digital environments involve different ways of conceiving and deploying interaction processes, inside and outside the classroom.This implies the imperative need to adjust the current processes of teacher training in such a way that teachers can understand these differences and recognize both their theoretical and practical implication.
Detecting Troll Behavior via Inverse Reinforcement Learning: A Case Study of Russian Trolls in the 2016 US Election

Since the 2016 US Presidential election, social media abuse has been eliciting massive concern in the academic community and beyond. Preventing and limiting the malicious activity of users, such as trolls and bots, in their manipulation campaigns is of paramount importance for the integrity of democracy, public health, and more. However, the automated detection of troll accounts is an open challenge. In this work, we propose an approach based on Inverse Reinforcement Learning (IRL) to capture troll behavior and identify troll accounts. We employ IRL to infer a set of online incentives that may steer user behavior, which in turn highlights behavioral differences between troll and non-troll accounts, enabling their accurate classification. As a study case, we consider the troll accounts identified by the US Congress during the investigation of Russian meddling in the 2016 US Presidential election. We report promising results: the IRL-based approach is able to accurately detect troll accounts (AUC=89.1%). The differences in the predictive features between the two classes of accounts enables a principled understanding of the distinctive behaviors reflecting the incentives trolls and non-trolls respond to.
BANANA: when Behavior ANAlysis meets social Network Alignment

Recently, aligning users among different social networks has received significant attention. However, most of the existing studies do not consider users’ behavior information during the aligning procedure and thus still suffer from the poor learning performance. In fact, we observe that social network alignment and behavior analysis can benefit from each other. Motivated by such an observation, we propose to jointly study the social network alignment problem and user behavior analysis problem. We design a novel end-to-end framework named BANANA. In this framework, to leverage behavior analysis for social network alignment at the distribution level, we design an earth mover’s distance based alignment model to fuse users’ behavior information for more comprehensive user representations. To further leverage social network alignment for behavior analysis, in turn, we design a temporal graph neural network model to fuse behavior information in different social networks based on the alignment result. Two models above can work together in an end-to-end manner. Through extensive experiments on real-world datasets, we demonstrate that our proposed approach outperforms the state-of-the-art methods in the social network alignment task and the user behavior analysis task, respectively.
Attentive Generative Adversarial Network To Bridge Multi-Domain Gap For Image Synthesis

Despite the significant progress on text-to-image synthesis, automatically generating realistic images remains a challenging task since the location and specific shape of object are not given in the text descriptions. To address these problems, we propose a novel attentive generative adversarial network with contextual loss (AGAN-CL) algorithm. More specifically, the generative network consists of two sub-networks: a contextual network for generating image contours, and a cycle transformation autoencoder for converting contours to realistic images. Our core idea is the injection of image contours into the generative network, which is the most critical part of our network, since it will guide the whole generative network to focus on object regions. In addition, we also apply contextual loss and cycle-consistent loss to bridge multi-domain gap. Comprehensive results on several challenging datasets demonstrate the advantage of the proposed method over the leading approaches, regarding both visual fidelity and alignment with input descriptions.
Generalized and Scalable Optimal Sparse Decision Trees

Decision tree optimization is notoriously difficult from a computational perspective but essential for the field of interpretable machine learning. Despite efforts over the past 40 years, only recently have optimization breakthroughs been made that have allowed practical algorithms to find optimal decision trees. These new techniques have the potential to trigger a paradigm shift where it is possible to construct sparse decision trees to efficiently optimize a variety of objective functions without relying on greedy splitting and pruning heuristics that often lead to suboptimal solutions. The contribution in this work is to provide a general framework for decision tree optimization that addresses the two significant open problems in the area: treatment of imbalanced data and fully optimizing over continuous variables. We present techniques that produce optimal decision trees over a variety of objectives including F-score, AUC, and partial area under the ROC convex hull. We also introduce a scalable algorithm that produces provably optimal results in the presence of continuous variables and speeds up decision tree construction by several orders of magnitude relative to the state-of-the art.
Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels

In weakly supervised learning, unbiased risk estimator(URE) is a powerful tool for training classifiers when training and test data are drawn from different distributions. Nevertheless, UREs lead to overfitting in many problem settings when the models are complex like deep networks. In this paper, we investigate reasons for such overfitting by studying a weakly supervised problem called learning with complementary labels. We argue the quality of gradient estimation matters more in risk minimization. Theoretically, we show that a URE gives an unbiased gradient estimator(UGE). Practically, however, UGEs may suffer from huge variance, which causes empirical gradients to be usually far away from true gradients during minimization. To this end, we propose a novel surrogate complementary loss(SCL) framework that trades zero bias with reduced variance and makes empirical gradients more aligned with true gradients in the direction. Thanks to this characteristic, SCL successfully mitigates the overfitting issue and improves URE-based methods.
How to Evaluate Health Applications with Conversational User Interface?

Application of conversational user interfaces (CUI) or chatbots to healthcare is gaining interest fueled by the rising power of artificial intelligence, increasing popularity of mobile health applications and the desire for engagement and usability. While their use is mainly justified by increasing adherence to mobile health applications and facilitating interactions with the system, the question arises: How can such systems be evaluated in a reliable manner? This paper introduces an evaluation framework for health systems whose core interaction principle is a CUI. We derive quality dimensions and attributes by collecting relevant evaluation aspects from applications that have been developed in previous work and from literature on health chatbots. The collected aspects are aggregated into six thematic categories for chatbot quality, including user experience, linguistic, task-oriented and artificial intelligence perspectives, but also healthcare quality and system quality perspectives. The framework is intended to support developers and researchers in the domain of chatbots in healthcare in selecting relevant quality attributes to be assessed before their systems are distributed to patients.
A differentially private greedy decision forest classification algorithm with high utility

The rapid development of data analysis technologies and the easily accessible datasets have enabled the construction of a comprehensive analytics model, which can facilitate the decision makings involved in services. Meanwhile, the individual privacy preservation is of great necessity. Decision tree is a common method in medical prediction and diagnose, known for its simplicity of understanding and interpreting. However, the process of building a decision tree might cause individual privacy disclosure. Differential privacy provides a rigorous mathematical definition of privacy by controlling the risk of privacy leakage in a manageable range while maintaining the statistical characteristics. In this paper, we propose a Differentially Private Greedy Decision Forest with high utility (DPGDF) to build a privacy-preserving decision forest. In DPGDF, we design a novel budget allocation strategy that allows the nodes in greater depth get more privacy budgets in the decision tree construction process, which can, to some extent, mitigate the problem of excessive noises introduced to the leaf nodes. To aggregate multiple trees into a forest, we propose a selective aggregation method based on the prediction accuracy of the decision forest. In addition, we develop an iterative method to speed up the process of selective aggregation. Finally, we experimentally prove that the proposed DPGDF can achieve a better performance on two practical datasets compared with other algorithms.
Predicting the Volume of Response to Tweets Posted by a Single Twitter Account

Social media users, including organizations, often struggle to acquire the maximum number of responses from other users, but predicting the responses that a post will receive before publication is highly desirable. Previous studies have analyzed why a given tweet may become more popular than others, and have used a variety of models trained to predict the response that a given tweet will receive. The present research addresses the prediction of response measures available on Twitter, including likes, replies and retweets. Data from a single publisher, the official US Navy Twitter account, were used to develop a feature-based model derived from structured tweet-related data. Most importantly, a deep learning feature extraction approach for analyzing unstructured tweet text was applied. A classification task with three classes, representing low, moderate and high responses to tweets, was defined and addressed using four machine learning classifiers. All proposed models were symmetrically trained in a fivefold cross-validation regime using various feature configurations, which allowed for the methodically sound comparison of prediction approaches. The best models achieved F1 scores of 0.655. Our study also used SHapley Additive exPlanations (SHAP) to demonstrate limitations in the research on explainable AI methods involving Deep Learning Language Modeling in NLP. We conclude that model performance can be significantly improved by leveraging additional information from the images and links included in tweets.
Learning Geometric Word Meta-Embeddings

We propose a geometric framework for learning meta-embeddings of words from different embedding sources.Our framework transforms the embeddings into a common latent space, where, for example, simple averaging or concatenation of different embeddings (of a given word) is more amenable.The proposed latent space arises from two particular geometric transformations -source embedding specific orthogonal rotations and a common Mahalanobis metric scaling.Empirical results on several word similarity and word analogy benchmarks illustrate the efficacy of the proposed framework.
GRSA: Service-aware flow scheduling for cloud storage datacenter networks

The proliferation of the global datasphere has forced cloud storage systems to evolve more complex architectures for different applications. The emergence of these application session requests and system daemon services has created large persistent flows with diverse performance requirements that need to coexist with other types of traffic. Current routing methods such as equal-cost multipath (ECMP) and Hedera do not take into consideration specific traffic characteristics nor performance requirements, which make these methods difficult to meet the quality of service (QoS) for high-priority flows. In this paper, we tailored the best routing for different kinds of cloud storage flows as an integer programming problem and utilized grey relational analysis (GRA) to solve this optimization problem. The resulting method is a GRAbased service-aware flow scheduling (GRSA) framework that considers requested flow types and network status to select appropriate routing paths for flows in cloud storage datacenter networks. The results from experiments carried out on a real traffic trace show that the proposed GRSA method can better balance traffic loads, conserve table space and reduce the average transmission delay for high-priority flows compared to ECMP and Hedera.
Improving symbolic regression based on correlation between residuals and variables

In traditional regression analysis, a detailed examination of the residuals can provide an important way of validating the model quality. However, it has not been utilised in genetic programming based symbolic regression. This work aims to fill this gap and propose a new evaluation criterion of minimising the correlation between the residuals of regression models and the independent variables. Based on a recent association detection measure, maximal information coefficient which provides an accurate estimation of the correlation, the new evaluation measure is expected to enhance the generalisation of genetic programming by driving the evolutionary process towards models that are without unnecessary complexity and less likely learning from noise in data. The experiment results show that, compared with standard genetic programming which selects model based on the training error only and two state-of-the-art multiobjective genetic programming methods with mechanisms to prefer models with adequate structures, our new multiobjective genetic programming method minimising both the correlation between residuals and variable, and the training error has a consistently better generalisation performance and evolves simpler models.
Dressing‐Controlled Quantum Steering in Energy‐Level Cascaded Parametric Amplified Four‐Wave Mixing Processes

Abstract Einstein–Podolsky–Rosen (EPR) steering for multipartite systems plays an important role in several quantum technologies and applications, e.g., quantum communication and quantum metrology. In this study, an energy‐level cascaded four‐wave mixing (EC‐FWM) process modulated by the dressing effect is proposed to generate three‐ and four‐mode EPR steering in the atomic medium of rubidium. The energy‐level simultaneously cascaded FWM and the energy‐level ordinally cascaded FWM are compared in three‐mode EPR steering, and monogamy relations for Gaussian steerability are also verified. Furthermore, multipartite EPR steerability is controlled by actively adjusting the dressing field. Different types of EPR steerability exhibit different modulation results when the dressing conditions of enhancement and suppression are satisfied. The proposed scheme exhibits advantages such as scalability, flexibility, and simplified experimental setup and can construct controllable large‐scale quantum networks.
Psychological and pedagogical aspects of increasing educational motivation

Given scientific research is devoted to the consideration of factors influencing educational (learning) motivation. One such factor is the assessment of academic achievements. The difficulties of obtaining an objective assessment in educational practice are noted and possible ways to overcome them are shown, including through external (independent) standardized testing. Authentic assessment requires special training and the creation of an appropriate base, this problem can be solved in the form of organizational and methodological support of teachers by external professional centers providing assessment in the form of an educational audit. The data of external testing and subsequent questionnaires are presented, allowing to judge about changes in the educational motivation of school students in the assessment process. The need to identify the progress of individual achievements in the educational process requires the development and introduction of new assessment forms that create the confidence of students in assessments and affect educationalmotivation.