Reviewerly/exHarmony huggingface dataset:
Collaborative museum heist with reinforcement learning

Abstract Non‐playable characters (NPCs) play a crucial role in enhancing immersion in video games. However, traditional NPC behaviors are often hard‐coded using methods such as Finite State Machines, Decision and Behavior trees. This has a few limitations; namely, it is quite difficult to implement complex cooperative behaviors and secondly this makes it easy for human players to identify and exploit patterns in behavior. To overcome these challenges, Reinforcement learning (RL) can be used to generate dynamic and real‐time NPC responses to human player actions. In this paper, we report on first results of applying RL techniques to a Non‐Zero Sum, adversarial asymmetric game, using a multi‐agent team. The game environment simulates a museum heist, where the objective of the successfully trained team of robbers with different skills (Locksmith, Technician) is to steal valuable items from the museum without being detected by the scripted security guards and cameras. Both agents were trained concurrently with separate policies and received both individual and group reward signals. Through this training process, the agents learned to cooperate effectively and use their skills to maximize both individual and team benefits. These results demonstrate the feasibility of realizing the full game where both robbers and security guards are trained at the same time to achieve their adversarial goals.
Enhancing Engineering Education with Augmented Reality Visualization

An augmented reality mobile app is an application that overlays digital content on top of the real-world environment through a mobile device's camera. Our project, named ARchino, is an augmented reality mobile application designed to assist users in visualizing hardware components in a 3D model format. The application utilizes the Vuforia engine for database management and Unity software for 3D model integration. The project aims to improve the user experience in understanding and identifying the hardware components by providing an interactive and immersive experience. The goal of this project is to develop an augmented reality (AR) app used for self-paced learning. This AR app allows IT students to learn about different components of computer hardware in a more interactive and engaging way. AR apps use 3D models of computer hardware components such as CPUs, servers, Keyboard, Mouse and other hardware components. Students can use AR app to view these components in a 3D layered structure, giving them a clear understanding of the various layers and their relationships. The AR app also allows students to rotate and zoom in/outon components, and learn about the different specifications of each component. Overall, this AR app has the potential to revolutionize engineering education by providing students with a more engaging and interactive way to learn about computer hardware
An accurate management method of public services based on big data and cloud computing

Abstract Information technology and resource grouping are developing rapidly thanks to governmental policies. Large-scale data mining is a hugely practical and scientific zone of “digital gold”. This is not only a modern-day problem, but it also necessitates the rapid growth of service of publics. As a result, the benefits of information can be fully realized, service efficiency improved, and the service gap closed. Cloud computing has gained popularity due to its quick accounting, limited memory requirements, and efficient resource distribution. As a result, big data was employed to analyze the existing state of cloud-based service of publics in this paper, and the fuzzy evaluation process was used to analyze the accurate monitoring. Eventually, service of public organization was improved, and the path to the accurate organization of the entire service of public process initiated by big data was developed. The design value and data entropy of cloud-based services of public were growing exponentially, according to fuzzy comprehensive assessment; the value of average of the design value of accurate organization of cloud-based government services was around 4.35, and the average importance of data entropy was around 0.98. Furthermore, the organization control and detailed efficiency of big data-driven precision organization of social public social welfare outperformed traditional precision monitoring of cloud-based service of publics; suggesting that the effects were 7% higher, and overall capacity was 9% higher than the compared techniques.
Virtual and Augmented Experience in Virtual Learning Tours

The aim of this work is to highlight the possibilities of using VR applications in the informal learning process. This is attempted through the development of virtual reality cultural applications for historical monuments. For this purpose, the theoretical framework of virtual and augmented reality techniques is presented, developing as a showcase of the virtual environment of the historical bridge of Arta, in Greece. The bridge model is created through 3D software, which is then imported into virtual world environment by employing the Unity engine. The main objective of the research is the technical and empirical evaluation of the VR application by specialists, in comparison with the real environment of the monument. Accordingly, the use of the application in the learning process is evaluated by high school students. Using the conclusions of the evaluation, the environment will be enriched with multimedia elements and the application will be evaluated by secondary school students as a learning experience and process, using electroencephalography (EEG). The recording and analysis of research results can be generalized and lead to safe conclusions for the use of similar applications in the field of culture and learning.
An object detection algorithm combining self-attention and YOLOv4 in traffic scene

Automobile intelligence is the trend for modern automobiles, of which environment perception is the key technology of intelligent automobile research. For autonomous vehicles, the detection of object information, such as vehicles and pedestrians in traffic scenes is crucial to improving driving safety. However, in the actual traffic scene, there are many special conditions such as object occlusion, small objects, and bad weather, which will affect the accuracy of object detection. In this research, the SwinT-YOLOv4 algorithm is proposed for detecting objects in traffic scenes, which is based on the YOLOv4 algorithm. Compared with a Convolutional neural network (CNN), the vision transformer is more powerful at extracting vision features of objects in the image. The CNN-based backbone in YOLOv4 is replaced by the Swin Transformer in the proposed algorithm. The feature-fusing neck and predicting head of YOLOv4 is remained. The proposed model was trained and evaluated in the COCO dataset. Experiments show that our method can significantly improve the accuracy of object detection under special conditions. Equipped with our method, the object detection precision for cars and person is improved by 1.75%, and the detection precision for car and person reach 89.04% and 94.16%, respectively.
The power of emojis: The impact of a leader’s use of positive emojis on members’ creativity during computer-mediated communications

The existing literature on emojis offers limited insights on the effects of using emojis in organizational settings, especially in the context of leader-member relationships. The current research examines how a leader's use of positive emojis can influence members' creative performance, a critical determinant of an organization's success and productivity. We find that a leader's use of positive emojis enhances members' creativity and that this effect is mediated by a decrease in members' perception of objectification by the leader. We further find that this impact of a leader's use of positive emojis on members' creativity is stronger when members have a higher level of relationship orientation. Contrary to the popular belief that the use of emojis in a work setting is inappropriate, our findings reveal that leaders' use of emojis has positive impacts on important workplace outcomes. These findings provide important guidelines on how to apply emojis to computer-mediated communications at work by demonstrating the circumstances in which positive consequences of using emojis occur.
The Digitalization of Cross-Border Payment Systems and the Introduction of the CBDC

China is the world's second-largest economy by GDP and the largest exporter, but its currency is not yet of global significance. By promoting the RMB as a global currency for trade, investment, and foreign exchange reserves, China aims to reap some of these benefits while increasing its economic policy autonomy. There are two main goals of the RMB internationalization. The first one is to enhance the status of the Chinese currency and strengthen its role against other currencies, especially against the USD. The second goal is to support the economic developments and to strengthen the role of China in the international economy. Due to the economic Sino-US decoupling, there is currently a much greater need for China to have its own strong international currency that supports the role of its economy and the strategies for further economic development. Some economists such as H. Chey, however, assess that it is almost impossible that the RMB will achieve a status similar to that of the USD in the near future. According to them, China has not developed deep, open, and liquid domestic capital markets, so the RMB is not widely used as a medium of exchange, unit of account and store of value at both official and private levels. Another characteristic of internationally operating currencies is the open and liberalized domestic economy, which is of limited relevance to China, which still has relatively closed capital flows. Apart from the traditional developments of currency internationalization, there is a growing role of the internationalization via digitalization. Digitalization of the RMB is a new Chinese initiative whose pace has increased in recent years. The main goal of the idea is to replace some of the cash in circulation by central bank digital currency (CBDC or e-RMB). The PBC is one of the first central banks across the world which pays attention to the idea of the CBDC. The country also became the first major economy which decided to create an official CBDC. It is expected that at the first stage of development, the e-RMB will be a domestic currency and the international use is not the current priority. However, it is planned that in the future the e-RMB will be used to navigate international transactions around payment systems and networks. The possibility to employ the CBDC in international transactions has important consequences for the process of RMB internationalization.
A Dynamic Finger Character Recognition Method Using Landmark Behavior Rule Base

This paper proposes a dynamic finger character recognition method using a landmark behavior rule base. By applying this rule base, not only static finger characters but also dynamic finger characters can be recognized, and all Japanese finger characters can be recognized. Japanese finger characters are finger character representations with hand finger shapes of the Japanese call letters in the Japanese language. The two main types of finger character forms are. One is the static finger character, which is represented by the shape of the hand without any motion, and the other is the dynamic finger character, which is represented by the shape of the hand and motion. Our method realizes recognition of both the static and dynamic finger characters, and continuous recognition. To achieve more flexible finger character recognition, it is required to recognize not only specific characters, but also all characters that can make up a word. The realization of automatic translation of sign language or finger characters will facilitate communication and social participation between both sign language and oral language speakers.
Automatic Modulation Recognition of Dual-Component Radar Signals Using ResSwinT-SwinT Network

Automatic modulation recognition plays an important role in military and civilian applications, identifying the modulation format of received signals before signal demodulation. With the increasing complexity and density of the electromagnetic environment, the multi-component modulation radar signal recognition against various signal-to-noise ratio (SNR) conditions has become a practical and urgent problem. In this paper, we propose a dual-component modulation recognition framework, which incorporates the residual Swin transformer denoise network (ResSwinT), Swin transformer feature extraction network (SwinT), residual-attention (RA) modulation recognition head, and SNR level classifier and achieves robust recognition performance against various SNR conditions with tolerable complexity and accuracy trade-off. Firstly, the time-frequency analysis is employed to transform dual-component radar signals into time-frequency images (TFIs). Then, the TFIs at various SNR levels are applied to the SwinT, which generates shallow and deep feature representations for the SNR classifier and RA-modulation recognition head, respectively. The ResSwinT is initiated to reconstruct low SNR TFIs only, which are again processed by the SwinT. Finally, the RA-modulation recognition head provides modulation format predictions. The proposed framework can identify randomly combined dual-component radar signals from 12 modulation formats, meanwhile, improving the utilization of the SwinT feature and reducing unnecessary computation of the ResSwinT. Simulation results show that the proposed scheme can obtain an exact match ratio (EMR) of larger than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\bm {97\%}$</tex-math></inline-formula> at SNR <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\bm {&gt;\!-6}$</tex-math></inline-formula> dB. At low SNR condition ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\bm {-12}$</tex-math></inline-formula> dB), the ResSwinT can obtain about EMR gain of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\bm {20\%}$</tex-math></inline-formula> and the overall framework can achieve EMR of more than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\bm {80\%}$</tex-math></inline-formula> , which outperforms other state-of-the-art methods and obtains better generalization capability.
Energy and Distribution-Aware Cooperative Clustering Algorithm in Internet of Things (IoT)-Based Federated Learning

In Internet of Things (IoT)-based federated learning (FL), if IoT devices are located far from the base station (BS), they consume lots of energy to transmit the updated parameters to BS, which can cause energy depletion of IoT devices. To mitigate this problem, IoT devices can be clustered and the clustered IoT devices transmit the updated parameters to their cluster headers (CHs) instead of BS. However, if the aggregated data distribution in each cluster is non-independent and identically distributed (non-IID), the desired accuracy of the model cannot be achieved. In this paper, we propose an energy and distribution-aware cooperative clustering algorithm (EDA-CCA) where several IoT devices with a sufficient energy level are selected as CHs. By considering the distance to these CHs and BS and the data distribution of IoT devices, other IoT devices are clustered, and then a hierarchical parameter aggregation (i.e., sequential aggregation within each cluster and synchronous aggregation between CHs and FL server) is conducted. Evaluation results demonstrate that EDA-CCA can make the model having the desired accuracy with the lowest energy consumption among the comparison schemes.
ATLAS: GAN-Based Differentially Private Multi-Party Data Sharing

In this article, we study the problem of differentially private multi-party data sharing, where the involved parties assisted by a semi-honest curator collectively generate a shared dataset while satisfying differential privacy. Inspired by the success of data synthesis with the generative adversarial network (GAN), we propose a novel GAN-based differentially private multi-party data sharing approach named ATLAS. In ATLAS, we extend the original GAN to multiple discriminators, and let each party hold a discriminator while the curator holds a generator. To update the generator without compromising each party's privacy, we decompose the calculation of the generator's gradient and selectively sanitize the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">discriminators’ responses</i> . Additionally, we propose two methods to improve the utility of shared data, i.e., the collaborative discriminator filtering (CDF) method and the adaptive gradient perturbation (AGP) method. Specifically, the CDF method utilizes trained discriminators to refine synthetic records, while the AGP method adaptively adjusts the noise scale during training to reduce the impact of deferentially private noise on the final shared data. Extensive experiments on real-world datasets validate the superiority of our ATLAS approach.
Open-Metric for Unknown Signal Inference

The closed-set assumption that only known classes can be classified during testing is usually adopted in traditional signal recognition missions. It has been thoroughly explored and made huge progress in the past decade. However, it cannot cope with the unknown categories coming from the changing environment. As waveform diversity technology evolves by leaps and bounds, the existence of novel unknown classes is inevitable in the open-world environment, imposing a huge challenge to the non-cooperative electronic support system. To further satisfy the demands of modern electronic intelligence systems and sense new threats, it is urgent to find an approach that can tackle unknown signals. An Open-Metric strategy is proposed in this article to deduce the unknown classes from the intercepted signals. By virtue of metric learning methods, a plain backbone can be trained to offer more discriminative representations and a consistent proxy for each class. According to extreme value analysis, information about known classes can be converted into an estimated score of unknown classes, creating an open decision layer. The investigated Open-Metric approach demonstrates its superiority in unknown signal inference tasks, facilitating improvements in the intelligence and smartness of the electronic reconnaissance agents to cope with future cognitive electronic warfare.
Communication-Efficient Federated Learning on Non-IID Data Using Two-Step Knowledge Distillation

Federated learning (FL) has shown its great potential for achieving distributed intelligence in privacy-sensitive IoT. However, popular FL approaches, such as FedAvg and its variants share model parameters among clients during the training process and thus cause significant communication overhead in IoT. Moreover, nonindependent and identically distributed (non-IID) data across learning devices severely affect the convergence and speed of FL. To address these challenges, we propose a communication-efficient FL framework based on Two-step Knowledge Distillation, Fed2KD, which boosts the classification accuracy through privacy-preserving data generation while improving communication efficiency through a new knowledge distillation scheme empowered by an attention mechanism and metric learning. The generalization ability of Fed2KD is analyzed from the view of domain adaption. Extensive simulation experiments are conducted on Fashion-MNIST, CIFAR-10, and ImageNet data sets with various non-IID data distributions. The performance results show that Fed2KD can reduce the communication overhead and improve classification accuracy compared to FedAvg and its latest variants.
Incorporating Semantics for Text Classification in Biomedical Domain

Text classification is an important task with its scope in many applications like biomedical domain, bioinformatics, text mining, information retrieval, etc. Different methods have been proposed in literature for classification of textual data into binary classes. Multi-label text classification on the other hand becomes a real challenge when the number of classes is high in number or even when the classification aims for higher accuracy. The complexity grows when the content is morphologically rich and yet the solutions to them are eagerly sought after. For decades, there have been extensive discussions on how to select the relevant features in text analytics or text mining. The paper proposes 3 categories for the selection of relevant features in biomedical text, namely statistical features, biomedical features and linguistics features to categorize the abstracts of biomedical literature. The proposed model implements the SVM classification algorithm on the dataset of Hallmark of Cancer with more than 1800 abstracts taken into consideration.
Improved Spectral Norm Regularization for Neural Networks

We improve on a line of research that seeks to regularize the spectral norm of the Jacobian of the input-output mapping for deep neural networks. While previous work rely on upper bounding techniques, we propose a scheme that targets the exact spectral norm. We evaluate this regularization method empirically with respect to its generalization performance and robustness. Our results demonstrate that this improved spectral regularization scheme outperforms L2-regularization as well as the previously used upper bounding technique. Moreover, our results suggest that exact spectral norm regularization and exact Frobenius norm regularization have comparable performance. We analyze these empirical findings in the light of the mathematical relations that hold between the spectral and the Frobenius norms. Lastly, in light of our evaluation we revisit an argument concerning the strong adversarial protection that Jacobian regularization provides and show that it can be misleading. In summary, we propose a new regularization method and contribute to the practical and theoretical understanding of when one regularization method should be preferred over another.
An Analysis of Word Sense Disambiguation (WSD)

Word sense disambiguation (WSD) is the method of using computer algorithms to determine the sense of arguments in the background. As a result of its difficult nature, WSD has measured an AI-complete problem, i.e., a problem whose key is as minimum as difficult as those posed by artificial intelligence. This article describes the task and introduces motives to resolve the ambiguity of words discussed throughout the text. This article summarizes supervised, unsupervised, and knowledge-based solutions. Senseval/semeval campaigns are described in relation to the assessment of WSDs, with the aim of an unbiased assessment of schemes working on numerous disambiguation errands. Finally, future directions, requests, open difficulties, and open problems are discoursed.