Reviewerly/exHarmony huggingface dataset:
HTN Guided Adversarial Planning for RTS Games

The real-time strategy (RTS) game has become a suitable testbed for artificial intelligence (AI) technologies owing to the large state space, limited decision times, partial observation environment and adversarial behavior involved. To address these challenges, the approach named AHTN which combined hierarchical task network (HTN) with game tree search has been applied in RTS games and achieved favorable performance. However, both the opponent strategy and time constraint of generating the sub-game tree are not considered by AHTN during planning. Therefore, this paper proposes a novel method which considers the time constraint when generating the subgame tree by HTN planning and introduces the opponent strategy as the adversarial player. The empirical results are presented based on a μRTS game and show our algorithm performs better than original HTN planning methods.
An adaptive method to correct the non-uniform illumination of images

It is known there are some problems such as the image suffers from excessive brightness or darkness under non-uniform illumination, loss of detail in the enhanced image, halo effect and so on. In this paper, an adaptive correction algorithm is proposed for these problems. Firstly, based on the illumination-reflection model, the illumination component was extracted by weighted guided filtering integrated with gradient information. Afterwards, the index of 2-d gamma curve is adjusted with the illumination component to realize the adaptive illumination correction. Finally, the output image pixels are produced with their adjacent pixels to enhance the local contrast of the image. The experimental results show three aspects. One is the improved weighted guided filtering can distinguish the edges adaptively and avoid the halo caused by excessive smoothness near the edges. Two is the clarity of enhanced image was improved by over 86.5% on average. At the same time, due to the reduction of color distortion, the ability to enhance image detail was improved by 18.5% on average.
The hydrothermal system associated with Miocene Chimpa volcano, Central Andes. Implications on potential mineralization

Miocene volcanism in the central sector of the Puna plateau, in the back–arc region of the Central Andes, has important economic potential, because it is associated with many epithermal ore deposits. Chimpa volcano is one of the largest stratovolcanoes of this region, hosting hydrothermal alteration. In order to map and characterize the hydrothermal alteration, multispectral ASTER satellite imagery, fieldwork, petrography, XRD and Raman spectroscopy have been used. Our study allowed identifying two alteration zones: alunite + illite + jarosite, and alunite + illite. The alunite + illite + jarosite zone occurs mainly in the center of Chimpa volcano, corresponding to the vent area and covering an area of ~0.02 km2. The alunite + illite zone covers a wider area mainly surrounding the alunite + illite + jarosite region, extending to the east and west and covering an area of ~2.5 km2. Small altered zones observed away from the vent area have been incorporated in mass-wasting and sector collapse deposits. Therefore, the hydrothermal alteration zone may be actually narrower than the area occupied by altered rocks. The absence of ore minerals indicates a potential barren hydrothermal system. However, the recognized alteration zones, together with the presence of vuggy silica and hypogene alunite, suggest that Chimpa volcano could host a volcanic-hydrothermal leached system related to the emplacement of a volcanic dome complex in the summit area. The hydrothermal system was set up in the final stage or immediately after the dome emplacement phase. Further prospection studies, such as geophysical techniques and drilling, should be focused on finding out if the Chimpa volcano hosts a mineralized system or is a non-economic acidic alteration.
MarioMix

In this paper, we propose a generic framework that enables game developers without knowledge of machine learning to create bot behaviors with playstyles that align with their preferences. Our framework is based on interactive reinforcement learning (RL), and we used it to create a behavior authoring tool called MarioMix. This tool enables non-experts to create bots with varied playstyles for the game titled Super Mario Bros. The main interaction procedure of MarioMix consists of presenting short clips of gameplay displaying precomputed bots with different playstyles to end-users. Then, end-users can select the bot with the playstyle that behaves as intended. We evaluated MarioMix by incorporating input from game designers working in the industry.
The optimal estimation state of one-axis twisting model

The possibility of estimating parameters and describing the optimal states induced from the one-axis twisting model on the thermal state was examined. The quantum Fisher information matrix be used as an estimator for multiple parameters. We demonstrate that concurrent estimation reduces estimation of parameter variance, and therefore the precision of estimating parameters simultaneously is much better than estimating them individually. Saturation is imaged at significant temperatures, although it increases the minimum variations in reaction force. It is shown that each parameter can be used as a control parameter to reduce the minimum variances for other parameters.
An Eye-Tracking Study of Differences in Reading Between Automated and Human-Written News

An eye-tracking experiment (N = 24) was conducted to study differences in reading between automated and human-written news. This work adopted expectation-confirmation theory to examine readers’ prior expectations and actual perceptions of both human-written news and automated news. Results revealed that nine eye-tracking variables were significantly different when people read automated news vs. human-written news. Findings also showed promising classification results of 31 eye-tracking-derived features. Self-reported results showed that the readability of human-written news was perceived as significantly higher than that of automated news.
Small and accurate heatmap-based face alignment via distillation strategy and cascaded architecture

Despite face alignment has made significant progress, it is still challenging to get a small and accurate face landmark detection model. In this paper, we focus on compressing heatmap-based face alignment algorithms using a novel proposed distillation strategy. We find that the activated areas of heatmaps generated from the well-trained teacher net can capture more local shape information of the facial parts than the ground-truth ones generated from the standard Gaussian distribution. To simultaneously transfer such shape information and correct the mis-predicted heatmaps generated from the teacher model, we first modify the heatmaps of teacher model by replacing the mis-predicted heatmaps with the ground-truth ones as the labels for the student net. To further improve the accuracy of the student net, we investigate the correlation between the extracted features and the predicted heatmaps, and divide the landmarks into two categories: simple and hard. A cascaded architecture is designed which firstly detects the simple points based on the extracted features, and then predicts the hard points resorting to the heatmaps of simple ones. Finally, a face alignment model with 3.64M parameters is obtained, which is about 6x smaller than the cumbersome model, and outperforms the state-of-the-art algorithms on both AFLW2000-3D and 300W-LP. The model and code are released in https://github.com/snow-rgb/Fast-Face-Alignment.
Information educational environment in the informatics teacher training system

Modern society changes daily under the influence of various factors. This inevitable process of development entails changes in all spheres of human activity, including education, which is the most important today, since it provides competitive specialists for the labor market and employment. In modern realities, a specialist must have not only fundamental knowledge, but also the skills and skills to apply them, as well as a wide range of additional qualities necessary for him in his professional activity, such as: the ability to self-development and self-education, the ability to master new information technologies, communication skills, be included in the process of mastering new trends in the field of their professional interests, and a number of others. A teacher is a specialist in the field of education who teaches and educates the younger generation. They must have all the above qualities, knowledge, skills and abilities in at least five subject areas (subject, methodology, pedagogy, psychology, information technology) and successfully combine all these to solve their professional tasks. The system of training a future teacher is quite a complex process due to its multitasking, but today an important aspect in this system is the information educational environment. The article describes the method of training a computer science teacher using this environment. The developed method was tested at the Department of Computational Mathematics and Methods of Teaching Informatics of Moscow Region State University and consists in creating special conditions that allow for joint information activities and interaction using various distributed information resources.
GELAB and Hybrid Optimization Using Grammatical Evolution

Grammatical Evolution (GE) is a well known technique for program synthesis and evolution. Much has been written in the past about its research and applications. This paper presents a novel approach to performing hybrid optimization using GE. GE is used for structural search in the program space while other meta-heuristic algorithms are used for numerical optimization of the searched programs. The hybridised GE system was implemented in GELAB, a Matlab toolbox for GE.
Verification of a Generative Separation Kernel

We present a formal verification of the functional correctness of the Muen Separation Kernel. Muen is representative of the class of modern separation kernels that leverage hardware virtualization support, and are generative in nature in that they generate a specialized kernel for each system configuration. We propose a verification framework called conditional parametric refinement which allows us to formally reason about generative systems. We use this framework to prove the correctness of Muen. Our analysis of several system configurations shows that our technique is effective in producing mechanized proofs of correctness, and also in identifying issues that may compromise the separation property.
FHAN: Feature-Level Hierarchical Attention Network for Group Event Recommendation

Recommending events to groups is different from to single-user in event-based social networks (EBSN), which involves various complex factors. Generally, group recommendation methods are either based on recommendation fusion or model fusion. However, most existing methods neglect the fact that user preferences change over time. Moreover, they believe that the weights of different factors that affect group decision-making are fixed in different periods. Recently, there are a few works using the attention mechanism for group recommendation. Although they take into account the dynamic variability of user preferences and the dynamic adjustment of user features weights, they haven’t discussed more features of groups and events affecting group decision-making. To this end, we propose a novel Feature-level Hierarchical Attention Network (FHAN) for group event recommendation for EBSN. Specifically, group decision-making factors are divided into group-feature factors and event-feature factors, which are integrated into a two-layer attention network. The first attention layer is constructed to learn the influence weights of words of group topics and event topics, which generates better thematic features. The second attention layer is built to learn the weights of group-feature factors and event-feature factors affecting group decision-making, which results in better comprehensive representation of groups and events. All influence weights of different features in the model can be dynamically adjusted over time. Finally, we evaluate the suggested model on three real-world datasets. Extensive experimental results show that FHAN outperforms the state-of-the-art approaches.
Stackelberg Game Based Edge Computing Resource Management for Mobile Blockchain

Blockchain can provide a dependable environment for mobile applications. As the core of blockchain, mining consumes a large amount of computing resources, while mobile devices are too resource-limited to perform the mining. Offloading mining computation tasks to an edge computing service provider (ESP) or a cloud computing service provider (CSP) is considered as a feasible solution to mobile blockchain mining. However, the computing resources of the ESP are not unlimited. Therefore, rational edge computing resource management is critical to maximizing the utilities of the ESP and the miners. Most of the existing work assumes that the service provider is an ESP or a CSP, or both. In this paper, we construct a computing offloading model consisting of multiple miners, an ESP, and a CSP, where the ESP and the CSP are independent of each other. We formulate a Stackelberg game with the ESP as the leader and the miners as the followers for optimal pricing-based edge computing resource management. We analyze the existence and uniqueness of Stackelberg game equilibrium and derive the miners' optimal computing resource requests. We then propose an effective golden section based Stackelberg game equilibrium searching algorithm (SES) for resource pricing. We conduct the experiments through simulations. The simulation results show that the proposed computing offloading model and algorithm can achieve high unit service utilities of both the ESP and the end devices.
A Framework to Secure Cluster-Header Decision in Wireless Sensor Network Using Blockchain

The Wireless Sensor Network (WSN) is hugely taking a big interest in worldwide in terms of security and private grants. Despite the increase of massive data transmission, a lot of challenges discovered in this technology, the data, and the critical information become vulnerable to a lot of malicious behaviors. In this paper, we proposed a new scheme to develop a dynamical framework to secure cluster-header decision-based blockchain in WSN that aims to facilitate the transactions and efficient to build a low-cost system. However, it applies the concept of Ethereum’s smart contracts to verify and check the confidentiality of each node in a decentralized architecture way. Otherwise, the implementation of the framework is deployed on the ganache environment using solidity as a programming language to write smart contracts to reach and resolve both the flexibility and efficiency of proof of work for each node. Consequently, the implementation results of the proposed scenarios confirm that this study achieves integrity, agreement, validity, availability, and termination which make the whole system a promising solution by decreasing building cost and improving the architecture effectiveness and trustworthiness.
Train in Dense and Test in Sparse: A Method for Sparse Object Detection in Aerial Images

Applications of aerial imaging, especially based on unmanned aerial vehicles (UAVs) platform, rapidly explode in recent years. Meanwhile, vision-based sensing, e.g., detection and recognition, for UAVs becomes increasingly important. Objects in aerial images are usually of tiny size, hence occupying a limited area. Terminology speaking, the images are very sparse in spatial. However, existing work in aerial object detection commonly ignores this point. Conversely, we explore the availability of such a property in improving the detection performance of aerial images. Specifically, we propose a general method, train in dense and test in sparse (TDTS), to exploit sparsity in aerial object detection: 1) in the training stage, the possible positions of object are learned by training a fully convolutional network (called prophet head) and 2) in the testing stage, prophet head identifies the possible object locations to reduce redundant computation in classification and box prediction head by sparse convolution. By extensive experiments on the VisDrone2019-Det data set, we find that the sparsity can not only help to speed up inference but also to improve accuracy. Thus, we argue that the sparsity deserves more attention.
A Comprehensive Trust Model Based on Social Relationship and Transaction Attributes

The existing approaches to predict trust values in social commerce are based on personal social relationships without considering historical transaction information about products in social commerce, which results in false recommendations, and deceptions cannot be differentiated. Trust values extracted from social links can improve the performance of trust and reputation mechanism, but the rates from these links in social commerce can be false because of the stakeholders’ manipulation for personal interest. And the rates are also dynamic and inconsistent. Therefore, this paper proposes a comprehensive trust model by fully exploiting the effects of the transaction attributes and social relationships on users’ trust. The proposed model refines the granularity of trust evaluation and improves the discrimination of recommended information. Experiments demonstrate that the proposed model performs better and predicts more accurately than the three models compared under the same circumstance.
An Hybrid Parallel Network Structure for Image Classification

Abstract The image classification task is to divide certain image target into the specific class which they belong to. The deep neural network simplifies image classification by sending input image data into the neural network to learn feature extraction and having the output results automatically. Existing network structures have been able to achieve or exceed human capabilities in image classification tasks. However, these image classification technologies still have problems such as over-fitting network model, insufficient generalization abilities and long convergence time. Interference caused by complex environments may also cause the performance degradation. This paper proposes a hybrid parallel neural network structure for this kind of artificial intelligence task. By adopting the K-parallel connection CNN structure and using different scales of RNN/CNN structure mixed-layer modes, the effects of image’s size on different objects and different shooting angles can be reduced in image classification. Experiments on the datasets show that the proposed structure will have the advantage of accuracy and computational efficiency both in the validation datasets and the test sets.
Analysis of the Influence of Stylized-CIFAR10 Dataset on ResNet

ResNet and other CNN networks have a very good performance in the field of image classification, and have developed rapidly in recent years.There are many explanations for the high performance of CNN, which are generally divided into two types: one is shape hypothesis, the other is texture hypothesis. It is found that the dependence of CNNs on texture or shape tends to come from datasets rather than model itself. In this paper, based on CIFAR10 dataset, the texture of the image is partially modified or completely removed, and the stylized image dataset with more shape information is generated. We carried out the experiments of different scale and various stylized-coefficient to study differences comprehensively and multilayered between the influence of stylized-cifar10 dataset with shape information and that of original one with both information and texture information.
User Activity Matters: An Activity Theory Informed Design Toolkit for Sustainable Behavior Design

Recent developments in eco-design have shown a growing interest in incorporating user perspectives in sustainable product and service design. However, users’ needs and behavior are not static, but under a dynamic transition process. Design purely informed by user needs thus may lead to environmental and social sustainability issues. In this paper, we approach this sustainable design challenge from an activity theoretical perspective. First, we conducted a literature review on the use of activity theory (AT) in sustainable design-related studies. Based on the literature insights, we translated the abstract AT concepts into more descriptive and practical design implications. Following that, we developed an activity-centered design (ACD) toolkit prototype to support design practitioners in integrating users’ dynamic activities with specific sustainable design goals in the early-stage design ideation process. Finally, we evaluated the practical use of the toolkit with both design experts and participants without a design background in a case study. Results indicated that the ACD toolkit prototype allowed participants to engage with complex sustainability issues while taking multiple aspects of users’ activity into account. It also offered an interactive way for designers to better develop early-stage design ideas to solve sustainability-related problems from a product and service design perspective.
Improved Dynamic Window Approach for Dynamic Obstacle Avoidance of Quadruped Robots

This paper studies dynamic obstacle avoidance strategies of quadruped robots in an unknown and dynamically changing environment. Different from classical navigation problems with static environment, an unknown number of moving obstacles with unknown dynamics are present in the scenario considered in this paper. This unknown and rapidly changing environment prevents us from directly applying existing navigation approaches for quadruped robots. To address the additional challenges introduced by the dynamic obstacles, an improved dynamic window approach (DWA) is proposed in which both evaluation function and constraints are modified. To enable real-time implementation of the proposed algorithm, multi-layer techniques for processing camera point cloud data are applied for online extraction of environmental information. The proposed algorithm is tested both in simulation and on hardware with a quadruped robot equipped with a low-cost RGB-D camera. The results show that the proposed algorithm significantly increases the rate of success for avoiding dynamical obstacles and reduces the time spent for reaching the target.
Perfect Quantum State Transfer in Glauber-Fock Cavity Array

We study transfer of single photon in an one-dimensional finite Glauber-Fock cavity array whose coupling strengths satisfy a square root law. The evolved state in the array can be mapped to an upper truncated coherent state if the cavities are resonant. Appropriate choices of resonance frequencies provide perfect transfer of a photon between any two cavities in the array. Perfect transfer of the photon allows perfect quantum state transfer between the cavities. Our findings may help in realizing quantum communication and information processing in photonic lattices.