Reviewerly/exHarmony huggingface dataset:
Untact: Customer’s Acceptance Intention toward Robot Barista in Coffee Shop

Restaurants have been using robots to meet the increasing consumer demand for food customization and safety and contactless service operations. This study identified the antecedent factors influencing a customer’s attitude and acceptance intention toward a robot barista. To this end, we conducted a questionnaire survey from 10 to 24 January 2020, on a sample of 317 Korean consumers who purchased coffee prepared by a robot barista. We based the analysis on the following determinant factors of the extension of the technology acceptance model (ETAM): perceived enjoyment, perceived usefulness, need for interaction, perceived risk, and perceived innovativeness. The results showed that perceived usefulness had the highest impact on acceptance intention, followed by perceived enjoyment and the need for interaction. Perceived usefulness and innovativeness positively influenced acceptance intention. These results confirmed the significance of the determinant factors in inducing customers’ attitude and acceptance intention toward a robot barista. This study extends the research on the application of artificial intelligence and the fourth industrial revolution technologies in the food service industry, and hence contributes toward the preparations for the post-Covid-19 period. It also offers practical implications for sustainable coffee shop management.
An Adaptive Stochastic Dominant Learning Swarm Optimizer for High-Dimensional Optimization

High-dimensional problems are ubiquitous in many fields, yet still remain challenging to be solved. To tackle such problems with high effectiveness and efficiency, this article proposes a simple yet efficient stochastic dominant learning swarm optimizer. Particularly, this optimizer not only compromises swarm diversity and convergence speed properly, but also consumes as little computing time and space as possible to locate the optima. In this optimizer, a particle is updated only when its two exemplars randomly selected from the current swarm are its dominators. In this way, each particle has an implicit probability to directly enter the next generation, making it possible to maintain high swarm diversity. Since each updated particle only learns from its dominators, good convergence is likely to be achieved. To alleviate the sensitivity of this optimizer to newly introduced parameters, an adaptive parameter adjustment strategy is further designed based on the evolutionary information of particles at the individual level. Finally, extensive experiments on two high dimensional benchmark sets substantiate that the devised optimizer achieves competitive or even better performance in terms of solution quality, convergence speed, scalability, and computational cost, compared to several state-of-the-art methods. In particular, experimental results show that the proposed optimizer performs excellently on partially separable problems, especially partially separable multimodal problems, which are very common in real-world applications. In addition, the application to feature selection problems further demonstrates the effectiveness of this optimizer in tackling real-world problems.
Analyzing the effectiveness of semi-supervised learning approaches for opinion spam classification

Opinion spam detection is concerned with identifying fake reviews that are deliberately placed to either promote or discredit a product. Opinionated social media like product reviews are increasingly important resources for people as well as businesses in the decision-making process and can be easily manipulated by opportunistic individuals. To reduce this increasing impact of opinion spams, opinion spam detection approaches have been proposed, which adopt mostly supervised classification methods. However, in practice, the provided data is largely not labeled and therefore semi-supervised learning approaches are required instead. To this end, this study aims to analyze the effectiveness of several semi-supervised learning approaches for opinion spam classification. Four different semi-supervised methods are evaluated on a dataset of both genuine and deceptive hotel reviews. The results are compared with several traditional classification methods using the same amount of labeled data. According to this study, the self-training algorithm with Naive Bayes as the base classifier yields 93% accuracy. Results show that self-training is the only approach, out of the four tested semi-supervised models, that outperforms traditional supervised classification models when limited data is available. This study further shows that self-training can mitigate labeling efforts while retaining high model performance, which is useful for scenarios where limited data is available or retrieving labeled data is more costly.
Finding new physics without learning about it: anomaly detection as a tool for searches at colliders

Abstract In this paper we propose a new strategy, based on anomaly detection methods, to search for new physics phenomena at colliders independently of the details of such new events. For this purpose, machine learning techniques are trained using Standard Model events, with the corresponding outputs being sensitive to physics beyond it. We explore three novel AD methods in HEP: Isolation Forest, Histogram-Based Outlier Detection, and Deep Support Vector Data Description; alongside the most customary Autoencoder. In order to evaluate the sensitivity of the proposed approach, predictions from specific new physics models are considered and compared to those achieved when using fully supervised deep neural networks. A comparison between shallow and deep anomaly detection techniques is also presented. Our results demonstrate the potential of semi-supervised anomaly detection techniques to extensively explore the present and future hadron colliders’ data.
Characterization and Prediction of Mobile-App Traffic Using Markov Modeling

Modeling network traffic is an endeavor actively carried on since early digital communications, supporting a number of practical applications, that range from network planning and provisioning to security. Accordingly, many theoretical and empirical approaches have been proposed in this long-standing research, most notably, Machine Learning (ML) ones. Indeed, recent interest from network equipment vendors is sparking around the evaluation of solid information-theoretical modeling approaches complementary to ML ones, especially applied to new network traffic profiles stemming from the massive diffusion of mobile apps. To cater to these needs, we analyze mobile-app traffic available in the public dataset MIRAGE-2019 adopting two related modeling approaches based on the well-known methodological toolset of Markov models (namely, Markov Chains and Hidden Markov Models). We propose a novel heuristic to reconstruct application-layer messages in the common case of encrypted traffic. We discuss and experimentally evaluate the suitability of the provided modeling approaches for different tasks: characterization of network traffic (at different granularities, such as application, application category, and application version), and prediction of network traffic at both packet and message level. We also compare the results with several ML approaches, showing performance comparable to a state-of-the-art ML predictor (Random Forest Regressor). Also, with this work we provide a viable and theoretically sound traffic-analysis toolset to help improving ML evaluation (and possibly its design), and a sensible and interpretable baseline.
A Probabilistic Compute Fabric Based on Coupled Ring Oscillators for Solving Combinatorial Optimization Problems

Nondeterministic polynomial time hard (NP-hard) combinatorial optimization problems (COPs) are intractable to solve using a traditional computer as the time to find a solution increases very rapidly with the number of variables. An efficient alternative computing method uses coupled spin networks to solve COP. This work presents a first-of-its-kind coupled ring oscillator (ROSC)-based scalable probabilistic Ising computer to solve NP-hard COPs. An integrated coupled oscillator network was designed with 560 ROSCs that mimic a coupled spin network. Each ROSC can be coupled to any of its neighbors using programmable back-to-back (B2B) inverter-based coupling mechanism. The ROSC-based spins and B2B inverter-based coupling were optimized to work under a wide range of system noise as well as voltage and temperature variations. Randomly generated 1000 max-cut problems were mapped and solved in the hardware. The integrated Ising computer produced satisfactory solutions of max-cut problems when compared with commercial software running on a CPU. Experiments show that the integrated CMOS-based Ising computer can find the solution to NP-hard problems with an accuracy of 82%-100%. In addition, the repeated measurements of the same problem showed that the Ising computer can traverse through several local minima to find high-quality solutions under various voltage and temperature variation conditions. The experimental results show that ROSCs are a potential candidate for a dedicated hardware accelerator aiming to solve a wide range of COPs.
MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization

This paper introduces MEDIASUM 1 , a largescale media interview dataset consisting of 463.6K transcripts with abstractive summaries.To create this dataset, we collect interview transcripts from NPR and CNN and employ the overview and topic descriptions as summaries.Compared with existing public corpora for dialogue summarization, our dataset is an order of magnitude larger and contains complex multi-party conversations from multiple domains.We conduct statistical analysis to demonstrate the unique positional bias exhibited in the transcripts of televised and radioed interviews.We also show that MEDIASUM can be used in transfer learning to improve a model's performance on other dialogue summarization tasks.
Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears

Abstract Technical advancements significantly improve earlier diagnosis of cervical cancer, but accurate diagnosis is still difficult due to various factors. We develop an artificial intelligence assistive diagnostic solution, AIATBS, to improve cervical liquid-based thin-layer cell smear diagnosis according to clinical TBS criteria. We train AIATBS with &gt;81,000 retrospective samples. It integrates YOLOv3 for target detection, Xception and Patch-based models to boost target classification, and U-net for nucleus segmentation. We integrate XGBoost and a logical decision tree with these models to optimize the parameters given by the learning process, and we develop a complete cervical liquid-based cytology smear TBS diagnostic system which also includes a quality control solution. We validate the optimized system with &gt;34,000 multicenter prospective samples and achieve better sensitivity compared to senior cytologists, yet retain high specificity while achieving a speed of &lt;180s/slide. Our system is adaptive to sample preparation using different standards, staining protocols and scanners.
CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language Understanding

Despite pre-trained language models have proven useful for learning high-quality semantic representations, these models are still vulnerable to simple perturbations.Recent works aimed to improve the robustness of pre-trained models mainly focus on adversarial training from perturbed examples with similar semantics, neglecting the utilization of different or even opposite semantics.Different from the image processing field, the text is discrete and few word substitutions can cause significant semantic changes.To study the impact of semantics caused by small perturbations, we conduct a series of pilot experiments and surprisingly find that adversarial training is useless or even harmful for the model to detect these semantic changes.To address this problem, we propose Contrastive Learning with semantIc Negative Examples (CLINE), which constructs semantic negative examples unsupervised to improve the robustness under semantically adversarial attacking.By comparing with similar and opposite semantic examples, the model can effectively perceive the semantic changes caused by small perturbations.Empirical results show that our approach yields substantial improvements on a range of sentiment analysis, reasoning, and reading comprehension tasks.And CLINE also ensures the compactness within the same semantics and separability across different semantics in sentence-level.
Dual Sparse Attention Network For Session-based Recommendation

Session-based Recommendations recommend the next possible item for the user with anonymous sessions, whose challenge is that the user’s behavioral preference can only be analyzed in a limited sequence to meet their need. Recent advances evaluate the effectiveness of the attention mechanism in the session-based recommendation. However, two simplifying assumptions are made by most of these attention-based models. One is to regard the last-click as the query vector to denote the user’s current preference, and the other is to consider that all items within the session are favorable for the final result, including the effect of unrelated items (i.e., spurious user behaviors). In this paper, we propose a novel Dual Sparse Attention Network for the session-based recommendation called DSAN to address these shortcomings. In this proposed method, we explore a learned target item embedding to model the user’s current preference and apply an adaptively sparse transformation function to eliminate the effect of the unrelated items. Experimental results on two real public datasets show that the proposed method is superior to the state-of-the-art session-based recommendation algorithm in all tests and also demonstrate that not all actions within the session are useful. To make our results reproducible, we have published our code on https://github.com/SamHaoYuan/DSANForAAAI2021.
CogTree: Cognition Tree Loss for Unbiased Scene Graph Generation

Scene graphs are semantic abstraction of images that encourage visual understanding and reasoning. However, the performance of Scene Graph Generation (SGG) is unsatisfactory when faced with biased data in real-world scenarios. Conventional debiasing research mainly studies from the view of balancing data distribution or learning unbiased models and representations, ignoring the correlations among the biased classes. In this work, we analyze this problem from a novel cognition perspective: automatically building a hierarchical cognitive structure from the biased predictions and navigating that hierarchy to locate the relationships, making the tail relationships receive more attention in a coarse-to-fine mode. To this end, we propose a novel debiasing Cognition Tree (CogTree) loss for unbiased SGG. We first build a cognitive structure CogTree to organize the relationships based on the prediction of a biased SGG model. The CogTree distinguishes remarkably different relationships at first and then focuses on a small portion of easily confused ones. Then, we propose a debiasing loss specially for this cognitive structure, which supports coarse-to-fine distinction for the correct relationships. The loss is model-agnostic and consistently boosting the performance of several state-of-the-art models. The code is available at: https://github.com/CYVincent/Scene-Graph-Transformer-CogTree.
LiftPose3D, a deep learning-based approach for transforming two-dimensional to three-dimensional poses in laboratory animals

Markerless three-dimensional (3D) pose estimation has become an indispensable tool for kinematic studies of laboratory animals. Most current methods recover 3D poses by multi-view triangulation of deep network-based two-dimensional (2D) pose estimates. However, triangulation requires multiple synchronized cameras and elaborate calibration protocols that hinder its widespread adoption in laboratory studies. Here we describe LiftPose3D, a deep network-based method that overcomes these barriers by reconstructing 3D poses from a single 2D camera view. We illustrate LiftPose3D’s versatility by applying it to multiple experimental systems using flies, mice, rats and macaques, and in circumstances where 3D triangulation is impractical or impossible. Our framework achieves accurate lifting for stereotypical and nonstereotypical behaviors from different camera angles. Thus, LiftPose3D permits high-quality 3D pose estimation in the absence of complex camera arrays and tedious calibration procedures and despite occluded body parts in freely behaving animals. LiftPose3D infers three-dimensional poses from two-dimensional data or from limited three-dimensional data. The approach is illustrated for videos of behaving Drosophila, mice, rats and macaques.
Informatization of education as a pledge of the existence and development of a modern higher education

This article focuses on the special significance of education informatization as the main aspect of the existence and development of a modern higher education. The process of computerization of education is considered as the main basis of informatization in the historical aspect. This paper emphasizes the importance of implementing information and communication technologies (ICT) in the learning process of free software and the interest of scientists in the field of education. The interest of modern scholars is analyzed in the consideration of such problems as the application of ICT in education; problems of informatization of education and goals of informatization of education; didactic and psychological aspects of application of ICT in the educational process; problems associated with the widespread introduction of ICT in higher education institutions and informatization of education in general. The article’s focus is on the importance of the acquired skills and abilities as a result of informatization of education and implementation of the educational process of ICT. The goals of informatization of education at a modern higher educational establishment are determined. The primary goals of informatization of education are singled out. The types of education that are directly related to ICT are considered. It is acknowledged that the practice of implementing ICT in the educational process of higher educational institutions are expanding every day and yields only positive results. The conclusions highlight the relevance of this study. It is noted that educational activity based on the use of ICT is a basis for changing the structure of the educational process for both teachers and students.
Boosting Whale Optimizer with Quasi-Oppositional Learning and Gaussian Barebone for Feature Selection and COVID-19 Image Segmentation

Whale optimization algorithm (WOA) tends to fall into the local optimum and fails to converge quickly in solving complex problems. To address the shortcomings, an improved WOA (QGBWOA) is proposed in this work. First, quasi-opposition-based learning is introduced to enhance the ability of WOA to search for optimal solutions. Second, a Gaussian barebone mechanism is embedded to promote diversity and expand the scope of the solution space in WOA. To verify the advantages of QGBWOA, comparison experiments between QGBWOA and its comparison peers were carried out on CEC 2014 with dimensions 10, 30, 50, and 100 and on CEC 2020 test with dimension 30. Furthermore, the performance results were tested using Wilcoxon signed-rank (WS), Friedman test, and post hoc statistical tests for statistical analysis. Convergence accuracy and speed are remarkably improved, as shown by experimental results. Finally, feature selection and multi-threshold image segmentation applications are demonstrated to validate the ability of QGBWOA to solve complex real-world problems. QGBWOA proves its superiority over compared algorithms in feature selection and multi-threshold image segmentation by performing several evaluation metrics.
Towards Grand Unification of Object Tracking

We present a unified method, termed Unicorn, that can simultaneously solve four tracking problems (SOT, MOT, VOS, MOTS) with a single network using the same model parameters. Due to the fragmented definitions of the object tracking problem itself, most existing trackers are developed to address a single or part of tasks and over-specialize on the characteristics of specific tasks. By contrast, Unicorn provides a unified solution, adopting the same input, backbone, embedding, and head across all tracking tasks. For the first time, we accomplish the great unification of the tracking network architecture and learning paradigm. Unicorn performs on-par or better than its task-specific counterparts in 8 tracking datasets, including LaSOT, TrackingNet, MOT17, BDD100K, DAVIS16-17, MOTS20, and BDD100K MOTS. We believe that Unicorn will serve as a solid step towards the general vision model. Code is available at https://github.com/MasterBin-IIAU/Unicorn .
Beyond task success: A closer look at jointly learning to see, ask, and

We propose a grounded dialogue state encoder which addresses a foundational issue on how to integrate visual grounding with dialogue system components.As a test-bed, we focus on the GuessWhat?! game, a two-player game where the goal is to identify an object in a complex visual scene by asking a sequence of yes/no questions.Our visually-grounded encoder leverages synergies between guessing and asking questions, as it is trained jointly using multi-task learning.We further enrich our model via a cooperative learning regime.We show that the introduction of both the joint architecture and cooperative learning lead to accuracy improvements over the baseline system.We compare our approach to an alternative system which extends the baseline with reinforcement learning.Our in-depth analysis shows that the linguistic skills of the two models differ dramatically, despite approaching comparable performance levels.This points at the importance of analyzing the linguistic output of competing systems beyond numeric comparison solely based on task success. 1
Detection and localization of image forgeries using improved mask regional convolutional neural network

The research on forgery detection and localization is significant in digital forensics and has attracted increasing attention recently. Traditional methods mostly use handcrafted or shallow-learning based features, but they have limited description ability and heavy computational costs. Recently, deep neural networks have shown to be capable of extracting complex statistical features from high-dimensional inputs and efficiently learning their hierarchical representations. In order to capture more discriminative features between tampered and non-tampered regions, we propose an improved mask regional convolutional neural network (Mask R-CNN) which attach a Sobel filter to the mask branch of Mask R-CNN in this paper. The Sobel filter acts as an auxiliary task to encourage predicted masks to have similar image gradients to the groundtruth mask. The overall network is capable of detecting two different types of image manipulations, including copy-move and splicing. The experimental results on two standard datasets show that the proposed model outperforms some state-of-the-art methods.