Reviewerly/exHarmony huggingface dataset:
Inspector: Pixel-Based Automated Game Testing via Exploration, Detection, and Investigation

Deep reinforcement learning (DRL) has attracted much attention in automated game testing. Early attempts rely on game internal information for game space exploration, thus requiring deep integration with games, which is inconvenient for practical applications. In this work, we propose using only screenshots/pixels as input for automated game testing and build a general game testing agent, Inspector, that can be easily applied to different games without deep integration with games. In addition to covering all game space for testing, our agent also tries to take human-like behaviors to interact with key objects in a game, since some bugs usually happen in player-object interactions. Inspector is based on purely pixel inputs and comprises three key modules: game space explorer, key object detector, and human-like object investigator. Game space explorer aims to explore the whole game space by using a curiosity-based reward function with pixel inputs. Key object detector aims to detect key objects in a game, based on a small number of labeled screenshots. Human-like object investigator aims to mimic human behaviors for investigating key objects via imitation learning. We conduct experiments on two popular video games: Shooter Game and Action RPG Game <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . Experiment results demonstrate the effectiveness of Inspector in exploring game space, detecting key objects, and investigating objects. Moreover, Inspector successfully discovers two potential bugs in those two games. The demo video of Inspector is available at https://github.com/Inspector-GameTestinyInspector-GameTesting.
Generalizations of Steering - A Modular Design

Steering and its many variants have been used to model multi-agent behavior in simulated worlds. Each variant of the steering algorithm introduces its own concepts and components. This work aims to generalize those in a modular building block design which allows to recreate existing steering algorithms and draft new versions. The concept is demonstrated in small-scale study on aggregation functions which shows the impact of a single building block on the agents’ behavior.
Causal Inference in Longitudinal Studies Using Causal Bayesian Network with Latent Variables

Longitudinal studies have been broadly used in clinical research to investigate the associations between exposures or treatments and the outcome of the diseases, such as disease onset, subsequent morbidity, and mortality. However, few studies emphasize the causal relationships between observed variables and latent, time-varying confounders. The causal Bayesian network (CBN) shows promise in handling multiple causes and effects. This paper presents an extension of the Bayesian Network for Latent Variable (BN-LV) framework that quantify the causal effects of the latent variables in CBNs by imposing various constraints for the identification of latent structures and the structure learning algorithms. The proposed model employs unit-level causal inference methods that can learn instance-specific causal mechanisms. The proposed model also provides “near” causality inference from the observational data, eliminating causal edges from the traditional BN-LVs framework. The method was validated using a case study: Temporal Associations Between Daytime Napping and Sleep Outcomes. The results showed the quantification for the average causal effects of napping on nocturnal sleep measures and the construction of a learned causal graph involving latent variables.
Space segmentation and multiple autonomous agents: a Minecraft settlement generator

This paper describes and illustrates a system designed as part of a submission to the Generative Design in Minecraft (GDMC) competition. It introduces an approach to partitioning of a three-dimensional game space novel to the domain of Minecraft settlement generation: traversal segmentation. Moreover, the paper introduces a novel implementation of a two-system brain model for autonomous agent simulation. Traversal segmentation is used in conjunction with a grid-wise segmentation method to produce a contextual representation of the game space. This is used as input for the settlement generation using autonomous agents, where each agent is controlled by their system 1 impulses, and their system 2 reasoning-action brain model. The two-system brain model is novel to autonomous agent simulation and is described both theoretically and by its implementation. The resulting settlements, generated by settlers upon grid-wise segmentation of the traversable space, boasts the properties; organic settlement evolution, and adaptability to its surrounding terrain, though not realism when compared to settlements procedurally generated by Minecraft.
An Empirical Study of User Engagement in Influencer Marketing on Weibo and WeChat

Understanding social media users’ engagement is one of the most crucial steps in a successful deployment of an influencer marketing campaign. On mainstream social media platforms, opinion leaders are the major channels to spread opinions and media content to a large population of consumers. Therefore, analyzing how users respond to the blogs of opinion leaders lies at the core of understanding user engagement in social media. In this article, to study the factors that have great impacts on user engagement, we first collect a cross-platform opinion leaders’ blogs dataset, which includes 344643 blogs published by 93 opinion leaders who have accounts on both Weibo and WeChat, the two most popular social media platforms in China. Based on this dataset, we conduct both characteristics study and semantic study to investigate the impact factors of user engagement with respect to blogs of opinion leaders. To find out the associations between user engagement and practically accessible attributes of opinion leaders and their blogs, we adopt state-of-the-art (SOTA) sentiment analysis models to process the blog data, develop a normalization technique to alleviate the issue caused by the heterogeneity of fall-out intervals of blogs, and devise a saliency method to compute the integrated gradients of sentences in blogs. Utilizing these computational tools, we reveal that user engagement on the two platforms agrees on some common factors, such as the number of tokens of blogs. Meanwhile, the two platforms differ in some aspects. For example, the semantic patterns that can improve the level of user engagement on the two platforms are very different. Our analysis can provide advertisers with valuable insights on how to plan an influencer marketing campaign on the two platforms.
Learning Event Extraction From a Few Guideline Examples

Existing fully supervised event extraction models achieve advanced performance with large-scale labeled data. However, when new event types emerge and annotations are scarce, it is hard for the supervised models to master the new types with limited annotations. In contrast, humans can learn to understand new event types with only a few examples in the event extraction guideline. In this paper, we work on a challenging yet more realistic setting, the few-example event extraction. It requires models to learn event extraction with only a few sentences in guidelines as training data, so that we do not need to collect large-scale annotations each time when new event types emerge. As models tend to overfit when trained with only a few examples, we propose knowledge-guided data augmentation to generate valid and diverse sentences from the guideline examples. To help models better leverage the augmented data, we add a consistency regularization to guarantee consistent representations between the augmented sentences and the original ones. Experiments on the standard benchmark ACE-2005 indicate that our method can extract event triggers and arguments effectively with only a few guideline examples.
MARNet: Backdoor Attacks Against Cooperative Multi-Agent Reinforcement Learning

Recent works have revealed that backdoor attacks against Deep Reinforcement Learning (DRL) could lead to abnormal action selections of the agent, which may result in failure or even catastrophe in crucial decision processes. However, existing attacks only consider single-agent reinforcement learning (RL) systems, in which the only agent can observe the global state and have full control of the decision process. In this article, we explore a new backdoor attack paradigm in cooperative multi-agent reinforcement learning (CMARL) scenarios, where a group of agents coordinate with each other to achieve a common goal, while each agent can only observe the local state. In the proposed MARNet attack framework, we carefully design a pipeline of trigger design, action poisoning, and reward hacking modules to accommodate the cooperative multi-agent settings. In particular, as only a subset of agents can observe the triggers in their local observations, we maneuver their actions to the worst actions suggested by an expert policy model. Since the global reward in CMARL is aggregated by individual rewards from all agents, we propose to modify the reward in a way that boosts the bad actions of poisoned agents (agents who observe the triggers) but mitigates the influence on non-poisoned agents. We conduct extensive experiments on three classical CMARL algorithms VDN, COMA, and QMIX, in two popular CMARL games Predator Prey and SMAC. The results show that the baselines extended from single-agent DRL backdoor attacks seldom work in CMARL problems while MARNet performs well by reducing the utility under attack by nearly 100%. We apply fine-tuning as a potential defense against MARNet and demonstrate that fine-tuning cannot entirely eliminate the effect of the attack.
Critique of “A Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery” by SCC Team From Peking University

Ankit Srivastava et al. (Srivastava et al. 2020) proposed a parallel framework for Constraint-Based Bayesian Network (BN) Learning via Markov Blanket Discovery (referred to as ramBLe) and implemented it over three existing BN learning algorithms, namely, GS, IAMB and Inter-IAMB. As part of the Student Cluster Competition at SC21, we reproduce the computational efficiency of ramBLe on our assigned Oracle cluster. The cluster has 4x36 cores in total with 100 Gbps RoCE v2 support and is equipped with CentOS-compatible Oracle Linux. Our experiments, covering the same three algorithms of the original ramBLe article (Srivastava et al. 2020), evaluate the strong and weak scalability of the algorithms using real COVID-19 data sets. We verify part of the conclusions from the original article and propose our explanation of the differences obtained in our results. <!--?query id="Q1"--> Author: Please confirm or add details for any funding or financial support for the research of this article. ?>
Cost-aware Resource Recommendation for DAG-based Big Data Workflows: An Apache Spark Case Study

The era of personal resources being sufficient for enterprise big data computations has passed. As computations are executed in the cloud, small policy changes of cloud operators may cause considerable changes in operational costs. Carefully choosing the amount of resources for a given application is thus of great importance. This, however, requires a priori knowledge of the application's performance under different configurations. Creating a performance prediction model needs to account for the heterogeneity of resources and the diversity in application workflows. Previous approaches for heterogeneous environments consider a black-box representation of the application which results in single-purpose models. This paper addresses the problem with two gray-box prediction models using linear programming (LP) and mixed-integer linear programming (MILP). Given a set of available resources, the models consider Apache Spark applications and their Directed Acyclic Graph (DAG) of workflow running on top of a Hadoop-YARN cluster. We then propose a configuration recommendation algorithm to optimize the cost-performance trade-offs when renting machine instances. The accuracy of the proposed models is evaluated with real-world executions of several representative applications on the Wikipedia dataset and the TPC-DS benchmark. The average error of only 3.28% for the proposed prediction models demonstrates the practicality of the proposed approach in handling cost-performance trade-offs.
An Effective Machine Learning Approach for English-Arabic Transliteration

Transliteration is the process of transferring a word from the alphabet of one language to the alphabet of another. The objective is to obtain a mapping from one system of writing into another, thereby helping people pronounce words and names in foreign languages and giving readers an idea of how words are pronounced by putting them in a familiar alphabet. In this paper, we explore recent trends in transliteration using deep learning models. We then adopt a convolution-networks’ seq2seq model developed by Facebook for the Arabic-English transliteration problem, and compare our approach against the previous ones. Our approach builds on recent work by Google and Amazon researchers and improves on previous methods both in the training and prediction steps.
Multi-Color Vehicle Tracking Based on Lightweight Neural Network

Vehicle tracking plays an important role in intelligent traffic management and criminal investigation assistance. At this stage, vehicle target detection technology has reached a relatively mature level of accuracy, but it is difficult to deploy to embedded platforms as the network depth increases. The computing power of the computer also has high requirements. In addition, the continuous increase of vehicle color richness increases the difficulty of target tracking, and it is impossible to identify color types that do not appear in the data set. In response to the above-mentioned problems, this paper proposes an improved YOLOv3 target detection algorithm that can be transplanted to the embedded side. Aiming at the shortcomings of the original YOLOv3 algorithm model that occupies a large amount of memory and is difficult to detect in real time on the embedded side, the lightweight MobileNetv2 depth reduce the network to replace the original YOLOv3 backbone network Darknet-53 for feature extraction, and at the same time make matching changes for anchors to adapt to the characteristics of the dataset for detection. Use the extracted self-built 24-color dataset for training, and then the experimental results show that the parameters and detection speed of the light-weighted YOLOv3 network is significantly better than that YOLOv3, and the recognition accuracy of the our dataset can reach 94.5%.
An Lévy Flight Based Honey Badger Algorithm for Robot Gripper Problem

The honey badger algorithm (HBA) is a recent meta-heuristic optimization algorithm that solves optimization problems by simulating the foraging behavior of honey badgers. To solve the poor convergence of this algorithm in the face of complex optimization problems and to improve the optimization performance of HBA, this paper proposes an enhanced Lévy based HBA algorithm and applies it to the optimization problem of the robot gripper. First, we improve the optimization efficiency of the basic HBA by using the Lévy flight strategy to enhance the local search capability and avoid falling into the local optimum. Secondly, we verify the performance of LHBA by the CEC2020 test function. The experiments show that the LHBA algorithm has good optimization ability. Finally, LHBA is used to solve the robot gripper optimization problem. The results show that LHBA can obtain the minimum value of the difference between the minimum force and the maximum force and successfully solve this optimization problem.
Digital Transformation in the Digital World the Metaverse

In this chapter, the author tries to explain the metaverse and identify factors affecting it. The factors were identified as business models, healthcare, leisure activities and social life, economy, ownership, education, freedom or ethic, and art. Each of these creations were affected by the metaverse, and they will be the backbone of building the metaverse. Metaverse is a new phenomenon which is changing the whole digital and physical environment in a very near time. With this respect, the author builds a frame of how the metaverse changes the world. The findings suggested that the economy, marketing, businesses model, and individuals' lives will change dramatically.
Prediction of Multi-Function Radar Signal Sequence Using Encoder-Decoder Structure

The multi-function radar (MFR) can perform a variety of tasks and has complicated and adaptable waveform characteristics. Predicting MFR transmission signals is critical for electronic warfare (EW) devices to develop effective countermeasures. This paper therefore deals with the signal sequence prediction problem in the presence of multiple targets via using an encoder-decoder structure. Specifically, the gated recurrent units (GRU) network is developed as the encoder to extract the temporal features, while the transformer decoder layers are exploited as the decoder to output the prediction results. The experimental results demonstrate that the method proposed can achieve effective prediction even in non-ideal situations.
Design and Implementation of Image Copyright Protection System Based on Chinese Cryptographic Algorithms

With the sharp increase in the number of netizens, people's online life is becoming richer and richer, the behavior of stealing pictures on the Internet is more rampant, and online image infringements continue to occur, so the protection of image copyright is particularly important. This paper first introduces the relevant background knowledge of Chinese cryptographic algorithms, image copyright protection and image steganography technology; secondly, the overall design scheme of the system is proposed, including functional design, hierarchical pattern design and system flow design; then, the key technologies of the system implementation are introduced; finally, test and analyze the system. The test results show that the system can secretly write the image copyright information encrypted by the SM2 algorithm into the image through the LSB algorithm, so as to achieve the purpose of image copyright protection without affecting the beauty of the image.
NIN-DSC: A Network Traffic Anomaly Detection Method Based on Deep Learning

Network traffic anomaly detection plays an important role in maintaining network space security and is usually modeled as a traffic classification problem. With the rapid change of the Internet, the network traffic environment is increasingly complex, and the traditional classification method is no longer applicable. In this article, we propose a new traffic classification framework for network anomaly detection, which operates directly on the original traffic. The NIN-DSC network was used to extract the features of the data, and the validation was carried out in the public data set.
Automatic Modulation Classification Strategy Based on Novel Feature Processing

The existing feature extraction algorithms have a poor classification effect on MPSK signals with low signal-to-noise ratio (SNR) region, and the measured value of feature changes greatly compared to the theoretical value. To solve these problems, this paper proposes a cascaded multi-classification parameter optimization system (CM-POS) optimized by the sparrow search algorithm, the system consists of two parts: a feature restorer built by a kernel function extreme learning machine (K-ELM) and a feature classifier built by a support vector machine. Based on the second, fourth, and sixth-order cumulants of the signal, extract new features of mixed cumulants: F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> and F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> to complete the classifications of {2PSK, 4PSK, 8PSK} signals. The new features are combined with classic features and entered into the restore system part of CM-POS. After the feature restoration processing, the classification accuracy of signal modulation methods is obviously improved in a low SNR region. Simulation results show that the optimization system can effectively restore the feature theoretical values of low SNR signals. When SNR is higher than -7dB, MPSK signals can be distinguished by the new features. When the SNR is higher than -1dB, the average classification accuracy of CM-POS for all signals can exceed 98.5%.
Mandarin Speech Recognition Based on Chinese Syllable Similarity for Children

Deep learning has facilitated the advancement and wide application of speech recognition technology. While mainstream speech recognition models are usually trained with adult standard speech data, which do not take into account the particularities of special groups, which is not suitable for all groups. Compared with adult speech, child speech recognition is more challenging because there are greater differences in acoustic and linguistic features between speakers. Furthermore, children have their own unique pronunciation habits and pronunciation methods and it’s hard for them to clear the difference between such as level and oblique tones, front nasal and back nasal. In addition, the lack of adequate and appropriate children’s voice resources increases the challenge of designing robust end-to-end neural structures. Therefore, we propose a novel model for Chinese children’s speech recognition, the Chinese children syllable similarity recognition model, which is used to help distinguish and identify children’s ambiguous pronunciation, in order to achieve a better recognition effect. Since the speech recognition of young children is usually short words and phrase recognition, the existing recognition evaluation metrics are not suitable. In the paper, we design a sliding window matching judgment method, which greatly improves the accuracy of children’s speech recognition and achieves very good recognition effect. Our model achieves a judgment accuracy of 91.7% on our dataset, which is higher than other models.