Reviewerly/exHarmony huggingface dataset:
Representing Cross-Cultural Links of Artifacts in Museums with Augmented Reality

Museums play a significant role in collecting and displaying artifacts from different cultures. As public spaces, museums also represent the society we live in and affect the public understanding of the past. Meanwhile, emerging technology such as Augmented Reality (AR) is changing how we perceive information, including in the museum space. This paper explores how AR can better help museum visitors understand the intercultural link between artifacts, by using Blue-and-White porcelain from Ming China and Safavid Iran as a case study. An AR prototype was developed during the process. This paper concludes with a discussion of the potential for AR as a powerful tool to solve problems in the current museology approach.
A novel permeability index prediction method for blast furnace based on the improved extreme learning machine

The permeability index of blast furnace reflects the operating conditions of the blast furnace and is an important basis for operators to judge whether the blast furnace is going forward. Based on K-ELM, this paper proposes a prediction model of permeability index based on KOS-ELM. Aiming at the shortcomings of KOS-ELM, such as high computational complexity and long learning time, this paper proposes an ALD-KOS-ELMF combining ALD and the sliding window. In the modeling process, taking into account the noise accompanying the blast furnace production data, this paper chooses the wavelet transform to reduce the noise inside the data. The experimental results show that the prediction model of permeability index based on ALD-KOS-ELMF not only improves the computational complexity and learning time, but also has better prediction accuracy and generalization performance than KOS-ELM. The model can meet the actual needs of relevant operators.
Spatial-temporal consistency knowledge distillation for real-time semantic segmentation

Real-time semantic segmentation is a key research topic in the application field of artificial intelligence such as automatic driving and intelligent robot. At present, the consumption of storage space and computing resources of the real-time semantic segmentation model is still huge. As an efficient model compression method, knowledge distillation is widely used in various fields of computer vision. In this paper, we propose a novel knowledge distillation framework based on generative adversarial network structure, which combines spatial consistency and temporal consistency. The teacher network in this framework jointly uses the CNN branch and transformer branch to improve the spatial consistency of lightweight real-time semantic segmentation of the student network. In addition, we integrate the inter-frame relationship obtained by the optical flow network and semantic segmentation network in continuous time as the time consistency constraint of the student network. Finally, spatial consistency and temporal consistency are coupled as spatial-temporal consistency knowledge. The main purpose of our knowledge distillation method is to transfer the spatio-temporal consistency knowledge contained by teachers to students. The student network obtained by knowledge distillation can process each frame independently in the inference stage, and our knowledge distillation method does not participate in the inference process of the student network, so it will not increase the computational cost of the student network in the inference process, but it can narrow the performance gap of real-time semantic segmentation between large model and compact model. Using our method, we can get a high-performance and efficient lightweight model. Finally, we verify the effectiveness of our proposed method on the Camvid dataset and the Cityscapes dataset.
Accuracy Analysis for Predicting Human Behaviour Using Deep Belief Network in Comparison with Support Vector Machine Algorithm

To detect human behaviour and measure accuracy of classification rate. Materials and Methods: A novel deep belief network with sample size 10 and support vector machine with sample size of 10. It was iterated at different times predicting the accuracy percentage of human behaviour. Results: Human behaviour detection utilizing novel deep belief network 87.9% accuracy compared with support vector machine 87.0% accuracy. Deep belief networks seem to perform essentially better compared to support vector machines <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${(\mathrm{p}=0.55)(\text{Pi}^{0.05}})$</tex> . The deep belief algorithm in computer vision appears to perform significantly better than the support vector machine algorithm. Conclusion: Within this human behaviour detection novel deep belief network has more precision than support vector machine.
Graph-QMIX: Addressing the Partial Observation Issues via Graph Neural Network in Multi-Agent Reinforcement Learning

In recent years, with the development of multiagent reinforcement learning, more and more complex tasks have been solved. However, today’s multi-agent reinforcement learning faces two challenges: 1) the global state is always used to train the neural network, which is hard to obtain in the real-world; 2) compared to the global state, concatenating local observations decreases the performance of multi-agent reinforcement learning algorithms. These challenges make it difficult to apply multi-agent reinforcement learning algorithms in real-world scenarios. To solve these challenges, we proposed the Graph-QMIX algorithm, where all agents are seen as a graph, and the graph convolutional neural network is used to integrate the local observations of the agents. We evaluate our method in map 2s vs lsc and map 10m vs 11m of SMAC environment. Empirically simulation results show that our method reaches a strong performance as much as QMIX using the global state, and is much stronger than QMIX using the concatenating local observations.
Assessing the Current State of AWS Spot Market Forecastability

Since 2009, Amazon has offered its unused compute capacity as AWS Spot Instances. For the first eight years of spot, pure market dynamics and high pricing variability created an ideal environment for time-series prediction. Following a pricing-scheme change in 2017, this extreme variability was removed as pricing is artificially smoothed for the end-user, therefore making it significantly easier to accurately predict prices. Nevertheless, the literature demonstrates ongoing efforts to accurately predict spot prices. To show prediction in the modern spot market is unnecessary, we train nearly 2.2 million ARIMA models on new and old data to demonstrate an order of magnitude improvement in accuracy for models trained on new data. Further, we show this new ease of price prediction makes spot instances ideal for large-scale, cost-aware cloud computing, as cost estimation is now trivial. Accordingly, we demonstrate that even naive prediction approaches waste less than $360 (2% of total costs) for 1,000,000 core hours.
Model Predictive Control of Autonomous Vehicles Using Sequence Convex Programming

A planner for autonomous vehicles is presented in this paper. The purpose of this algorithm is to plan a collision-avoidance trajectory with cheap computation. We consider model predictive control (MPC) of the vehicle in the presence of obstacles, in which the optimization usually admits nonlinear programming. By introducing sequential programming (SCP), the nominal trajectory dependent by convexification is updated in time, then the non-convex optimization problem is approximately solved. The approach is validated through simulation compared with regular nonlinear MPC formulation solved by numeric solver.
Learning Landmark-Oriented Subgoals for Visual Navigation Using Trajectory Memory

In many deep reinforcement learning (DRL) applications, agents must perform complex and long-horizon tasks that are still challenging in DRL because of temporally ex-tended tasks with sparse rewards. Goal-conditioned hierarchical reinforcement learning (HRL) is a promising approach for control at multiple time scales via a hierarchical structure with subgoals. One of the key issues of goal-conditioned HRL is the definition of subgoals. In this study, we propose a DRL model for learning landmark-oriented subgoals using attention-augmented trajectory memory. In our approach the agent is trained to make decisions based on both 1) current perception, which is a short-term temporal history derived from vanilla long short-term memory (LSTM) and 2) trajectory memory, which represents a contextual summary of long-term historical LSTM states augmented by attention. The experiment on a visual navigation task shows that the short-term LSTM state of the current perception module extracts landmark subgoals as clusters that correspond to lower-level policies, and the long-term context states of trajectory memory extract subgoal transitions that correspond to higher-level policies. Furthermore, the proposed method demonstrated superior adaptability to environmental changes,
Autoscaling of Containerized HPC Clusters in the Cloud

This paper introduces a Cloud orchestrator controller that enables the autoscaling of containerized HPC Clusters in the Cloud. This controller triggers the creation or suppression of containerized HPC compute nodes according to metrics collected at the containerized HPC scheduler's job queue level. This paper uses Kubernetes as the Cloud orchestrator and OAR as the HPC scheduler, and our approach does not modify the Kubernetes Cloud orchestrator or the OAR HPC scheduler. The scheme followed in this paper is generic and can be applied to other HPC schedulers. We assume that containerization principles facilitate the reproducibility of experiments by adding the HPC scheduler to the environment replayed by the end user. The paper exemplifies Cloud and HPC convergence to allow a high degree of flexibility for users and community platform developers. It also explores continuous integration/deployment approaches of Cloud computing to orchestrate multiple and potentially different HPC job schedulers that scale under the supervision of the Cloud Orchestrator. The experimental part of the work is highly reproducible, demonstrating the advanced nature of our research work.
Modified Annealed Adversarial Bonus for Adversarially Guided Actor-Critic

This paper investigates learning efficiency for rein-forcement learning in procedurally generated environments. A more sophisticated method is proposed to adjust the adversarial bonus to promote learning efficiency instead of the linearly decayed scheme in adversarially guided actor-critic. Our method considers the relationship between the bonus adjustment and the learning procedure. In some environments, if an agent performs better in learning, the agent will reach the goal with fewer steps. If the length of the episode decreases, the adversarial bonus will be reduced in our method. In this way, the learning efficiency has been improved in some procedurally generated tasks. Several experiments are implemented in MiniGrid to verify the proposed method. In the experiments, the proposed method outperforms the existing adversarially guided methods in several challenging procedurally-generated tasks.
Dance Syntax and Possibility: Moving Beyond Structural Analysis

Abstract In this work we contend that studying dance syntax systematically is essential to gain a deeper understanding of dance practices. The reason is that syntax has to do with an essential aspect of dance, music and action in general, namely possibility . To the best of our knowledge, the efforts towards a systematic method to study dance syntax are scarce. Therefore, this work proposes the method of Finite-State Automata, borrowed from computer science, and presents three case studies of progressive complexity were the method is applied: (1) learning the basics of salsa, (2) diachronically comparing hip-pushing action in Afro-Ecuadorian Bomba del Chota, and (3) characterizing improvisation in Afro-Peruvian zapateo. While the first case is didactic and introduces the method progressively, the second and third cases are based on several years of fieldwork conducted by the authors with the Afro-Ecuadorian and Afro-Peruvian communities. The precondition for the application of the method we propose is structural analysis itself; that is, that the dance can be analyzed into small movement units that are combined progressively into more complex units. In regards to syntax, however, structural analysis is only the first step. The goal is a synthesis that brings forward the possibilities that arise from structural analysis; that is, the possibilities that are available to dancers and agents in a dance event. We trust that the approach to syntax this work presents will stimulate a renewed interest for researchers in dance, music and movement in general.
The Role of Prototypical Transitivity in the Selection of Accusative Case Particle wo by Persian Learners of L2 Japanese

This study attempts to account for Japanese language learners’ selection pattern of case particle wo by taking a prototype-based approach to language learning. A cross-sectional study of Persian-speaking learners was conducted to examine whether the degree of predicate transitivity influences case particle selection, and whether proficiency level and L1 play a role in such a selection pattern. The results reveal that learners were sensitive to the degree of predicate transitivity. However, the test scores were significantly higher for verbs of contact (‘throw’, ‘use’) which express object manipulation compared to those for the linguistic prototype which involves a change in the object’s state (‘cut’, ‘reduce’). This suggests that learners may consider contact verbs as the more ‘typical’ or basic transitive construction. The results also show that lower proficiency learners were more likely to select particles based on L1 surface marking, suggesting that learners’ particle selection strategy is influenced by proficiency level.
How College Students Interpret and Use Social Media as a Potential Source of Sexual Consent Communication

This study investigated how social media use, specifically exposure to and posting of sexualized and party-related content on social media and interpretations of that content as sexual consent communication, related to college students’ intentions to engage in sexual consent communication. A national sample of U.S. college students ( N = 954) completed the relevant measures in an online survey. Results indicated that the more college students reported exposure to sexualized and party-related content on social media, the more likely they were to report posting similar content and believe in harmful consent myths (i.e., that a person’s sexual consent can be assumed by looking at their social media profiles). Posting sexualized and party-related content on social media was also related to lesser intentions to engage in consent communication. These findings provide evidence for the importance of considering social media use in how college students understand sexual consent communication that may be used to inform future sexual consent education on college campuses.
Prototyping an Architecture of Affective Robotic Systems Based on the Theory of Constructed Emotion

Recognizing emotions is one of the most difficult tasks for computers. Most emotion theories claim that basic emotions are genetically endowed, whereas the theory of constructed emotion states that our brain constantly uses past experiences to guide our actions and construct emotions and generates in each situation a new instance of emotion. This allows us to describe emotions in terms of multidimensional values – valence, arousal, and dominance. By describing emotion instances in terms of these dimensions, we can start comparing different emotional states based on the user input and reflect by a computational system on the emotional state of the user. This paper describes the design, implementation, and validation of a chatbot that can recognize the emotions of its human user and generates replies based on the current emotional state of the user perceived by it. The purpose of the chatbot is to prototype an emotion-aware social robot, which is based on the theory of constructed emotion.
Socially-Aware Mobile Robot Trajectories for Face-to-Face Interactions

In this paper, we demonstrate through user studies that mobile robot trajectories that imitate human-to-human approach trajectories are perceived more socially acceptable in the face-to-face interaction scenario than those imitating point-to-point trajectories. We generate robot trajectories to/from a human standing at an arbitrary location by applying inverse optimal control to a human-to-human trajectory dataset. The cost function used in a previous work for modeling human point-to-point trajectories does not represent human-to-human trajectories due to the circular paths often observed around the target human. We therefore propose a new cost function motivated by the social force model. The user study confirms that the resulting trajectories are more preferred with statistical significance than baseline.
Privacy-Preserving Decision Tree Classification Protocol Based on Bitwise Comparison

Decision tree model is widely used in telemedicine, credit evaluation and other fields because of its high efficiency and ease of use. Companies can charge customers who do not have professional knowledge or resources to build pre-diction models to achieve the purpose of profitability. In order to protect the privacy of client data and decision tree model parameters in this scenario, we propose an integer comparison protocol based on distributed bitwise comparison, and further design an efficient privacy-preserving decision tree classification model. Our protocol uses the idea of Beaver triple multiplication combined with secret sharing to replace the ciphertext homomorphic operation in the original comparison protocol. Compared with the comparison protocol re-lying on the traditional semi-homomorphic encryption scheme, it further im-proves the operation efficiency and compresses the communication cost in ciphertext transmission. Security analysis shows that this scheme achieves the expected privacy for users and model providers. Experimental results on real datasets show that the overall running time of this scheme is about 0.8s for a decision tree of depth 3, and about 5s for a decision tree of depth 17. So, its computational overhead is low and acceptable.
A Bi-objective Source Hiding Method for Network Propagation

In many scenarios, sources want to publish information anonymously and allow it to be distributed on a large scale in a short period of time. To this end, we proposed a bi-objective source hiding method to locate the sources with both hiding and propagation capability in the network. Firstly, three heuristic methods are proposed to evaluate the hiding performance of nodes. Secondly, based on the characteristics of network propagation, breadth first search is applied to measure the propagation capability of nodes. Then, a normalization method is proposed to comprehensively evaluate the hiding ability and propagation ability of nodes, and the node with the strongest comprehensive ability is used as the propagation source. Finally, experiments are simulated based on five different source detection methods on multiple network structures, and the experimental results demonstrate the effectiveness of our method.
Event Annotation Enhanced Pre-trained Language Model in Event Extraction

Event extraction is a crucial task that aims to extract event information in texts. Existing methods usually use pre-trained language models to extract events and have achieved state-of-the-art performance. However, these models do not consider the complexity of the event structure and lack the use of event knowledge. To address these problems, we propose a new framework that integrates event annotation into the pre-trained model explicitly, termd as EABERT. Specifically, event annotations are incorporated into the model input to construct the form “[CLS]sentence[SEP]event annotation[SEP]”, which allows the model to encode the semantic relationship between text and event knowledge. To incorporate appropriate event annotations into the model, we further use the bilateral-branch BERT network to train the event type classifier for better accuracy of event annotations. Experiments on the event extraction benchmark dataset (ACE 2005) show that our proposed framework has significantly improved compared to previous methods.
Dynamic Style Transferring and Content Preserving for Domain Generalization

Although convolutional neural networks (CNNs) have shown remarkable ability in different computer vision tasks, they do not cope well with domain shifts. Recent studies show that the domain shift mainly results from the style or texture variation of images rather than the content. Inspired by this, we propose dynamic style transferring to overcome the style bias of CNNs. Specifically, we design a knowledge-injected attention mechanism for learning adaptive fusion weights and embedding the style knowledge of dynamic chosen images in latent space. So the extent of transferred style is controlled, and we can retain content-related information. Furthermore, we introduce the content preserving module, which builds an adversarial structure with the encoder to make the extracted style information more precise. For balancing the adversarial relationship between encoder and auxiliary predictor, we also introduce a consistency loss to empower the style-biased predictor and indirectly boost the encoder’s ability by extending the back-propagation process. We conduct extensive experiments on PACS and Office-Home datasets to evaluate the effectiveness of our method. Experiment results show remarkable performance over the state-of-the-art methods in the domain generalization.
A Survey on Intelligent Question and Answer Systems

With the rapid development of technology in society, people are surrounded by all kinds of data in the information age. So the means to access external information accurately and quickly have become particularly important. Currently, intelligent question and answer systems are a promising area of research in the field of artificial intelligence and natural language processing. As an interactive system, it significantly differs from traditional search engines. When people perform advanced information retrieval, it can accurately understand the natural language questions asked by the user and give the user a corresponding answer using natural language, which meets people’s needs for quickly, easily and accurately accessing information. Intelligent question and answer systems are already widely used in people’s daily life, such as common intelligent voice interaction, online customer service, knowledge acquisition, emotional chat, etc. Deep learning, a branch of machine learning, is now widely used for various tasks in natural language processing. This paper focuses on the applications of deep learning to intelligent question and answer systems.