Reviewerly/exHarmony huggingface dataset:
Digital economy of China: level and factors of development

The article considers the active development and significant impact of digital technologies on people’s lives, functioning of enterprises in various sectors of the economy and government agencies. The level of digital competitiveness is different, which determines the importance of studying and adapting the experience of countries - digital leaders with a developed and rapidly growing digital economy of China. There has been given analysis of the main indicators of the development of China’s digital economy over the period 2018-2020, which showed high growth rates of the digital economy - more than 25%, an increase in the share of the digital economy in GDP by almost 6 p.p. There have been defined the policies, in which China is the undisputed world leader: e-commerce, financial technology, cloud computing, and the export of IT products. China’s strengthening its position in international digital rankings has been analyzed. It was ranked 4th in the Digital Opportunity Index, 16th in the Global Digital Competitiveness Index and 45th in the E-Government Development Index. This became possible due to factors such as the scale and potential of the domestic market, a high level of technological development, an active innovation policy of the state and large Chinese corporations that promotes digitalization, high quality education and research results. Taking into account the processes taking place in the global economy in 2020, the short-term and long-term effects of the COVID-19 pandemic on the development of China’s digital economy have been identified. The short-term effects include the capitalization of restrictions, testing of new technologies and familiarization of the population with new digital opportunities, the long-term ones are the expansion of demand and a change in its structure, an increase in investments in digital transformation, including through public and private support programs
One Source, Two Targets: Challenges and Rewards of Dual Decoding

Machine translation is generally understood as generating one target text from an input source document.In this paper, we consider a stronger requirement: to jointly generate two texts so that each output side effectively depends on the other.As we discuss, such a device serves several practical purposes, from multi-target machine translation to the generation of controlled variations of the target text.We present an analysis of possible implementations of dual decoding, and experiment with four applications.Viewing the problem from multiple angles allows us to better highlight the challenges of dual decoding and to also thoroughly analyze the benefits of generating matched, rather than independent, translations.
Recreating Traditional Indonesian Batik with Neural Style Transfer in AI Artistry

Style transfer is a method of combining two images into one, taking reference of one of the image’s styles and the other image’s content. Convolutional neural networks have been applied to this method to produce what is known as neural style transfer. Using VGG-Network, the artificial system was able to recreate artistic images by combining different content and style. However research regarding the effects of different models and optimization to the quality of the image produced are limited. The aim of this experiment is to compare between the VGG19 and VGG16. We use data and results from Leon A. Gatys to compare between two different models which are VGG19, and VGG16 in terms of content loss and style loss. This architecture is also applied to the more focal style of Batik in this to experiment the effects of a dominant color and pattern on another image. With Indonesia’s rich culture and its diverse art portfolio, it is only natural that this paper explore neural style transfer’s effects on the creative pattern forming of Batik.
A Hybrid Machine Learning Approach for Performance Modeling of Cloud-Based Big Data Applications

Abstract Nowadays, Apache Hadoop and Apache Spark are two of the most prominent distributed solutions for processing big data applications on the market. Since in many cases these frameworks are adopted to support business critical activities, it is often important to predict with fair confidence the execution time of submitted applications, for instance when service-level agreements are established with end-users. In this work, we propose and validate a hybrid approach for the performance prediction of big data applications running on clouds, which exploits both analytical modeling and machine learning (ML) techniques and it is able to achieve a good accuracy without too many time consuming and costly experiments on a real setup. The experimental results show how the proposed approach attains improvement in accuracy, number of experiments to be run on the operational system and cost over applying ML techniques without any support from analytical models. Moreover, we compare our approach with Ernest, an ML-based technique proposed in the literature by the Spark inventors. Experiments show that Ernest can accurately estimate the performance in interpolating scenarios while it fails to predict the performance when configurations with increasing number of cores are considered. Finally, a comparison with a similar hybrid approach proposed in the literature demonstrates how our approach significantly reduce prediction errors especially when few experiments on the real system are performed.
ConvLSTM Neural Networks for seismic event prediction in Chile

Predicting seismic risk is a challenging task in order to avoid catastrophic effects. In this work, two models based on Convolutional Network (CNN) and Long Short Term Memory (LSTM) networks are proposed to predict the seismic risk in Chile. In particular, a ConvLSTM and a Multi-column ConvLSTM network are used for the prediction of the average number of seismic events greater than 2,8 magnitude on the Richter scale, in the Chilean regions of Coquimbo and Araucania between the years 2010 and 2017. For this model, the values of the intensity function estimated through an ETAS model and the accumulated displacement prior to a the seismic events are used as inputs. In particular, given the spatial and temporal characteristics of the seismic data, matrices of size 20x20 of the last 20 days are considered to predict the average number of seismic events of the next day in a given area. From the results obtained, the Multi-column ConvLSTM network achieved a coefficient of determination of 0,804 and a lower MSE than other networks.
Independent Deep Deterministic Policy Gradient Reinforcement Learning in Cooperative Multiagent Pursuit Games

In this paper, we study a fully decentralized multi-agent pursuit problem in a non-communication environment. Fully decentralized (decentralized training and decentralized execution) has stronger robustness and scalability compared with centralized training and decentralized execution (CTDE), which is the current popular multi-agent reinforcement learning method. Both centralized training and communication mechanism require a large amount of information exchange between agents, which are strong assumptions that are difficult to meet in reality. However, traditional fully decentralized multi-agent reinforcement learning methods (e.g., IQL) are difficult to converge stably due to the dynamic changes of other agents' strategies. Therefore, we extend actor-critic to actor-critic-N framework, and propose Potential-Field-Guided Deep Deterministic Policy Gradient (PGDDPG) method on this basis. The agent uses the unified artificial potential field to guide the agent's strategy updating, which reduces the uncertainty of multi-agent's decision making in the complex and dynamic changing environment. Thus, PGDDPG which we proposed can converge fast and stably. Finally, through the pursuit experiments in MPE and CARLA, we prove that our method achieves higher success rate and more stable performance than DDPG and MADDPG.
Efficient Subgraph Pruning &amp; Embedding for Multi-Relation QA over Knowledge Graph

The intelligent question answering over the knowledge graph aims to automatically answer natural language questions via locating the correct entities in the knowledge graph. Aside from the former progresses, it is still challenging to answer the multi-relation questions because of the variety and complexity of the natural language, as well as the combinatorial explosion on possible candidates. In this paper, we propose a novel embedding-based approach named SPE-QA to address these issues. It answers a question by identifying its most semantic like question-answer path from the candidate topic-entity-centric subgraph, and locating this path's tail entity as the final answer. In order to limit the scale of the candidate sub graph and thus reduce the neural network's training complexity, it is essential to filter the explicit noises as much as possible. Therefore, we employ a sub graph separation method to decide the scale of the sub graph, and remove the false question-answer paths with two different pruning policies. The experimental results on two widely used benchmarks approve that, our mechanism can not only reduce the subgraph's scale dramatically, but also obtain better performance on the multi-relation questions, compare to the stat-of-the-art approaches.
High-Accuracy Gaze Estimation for Interpolation-Based Eye-Tracking Methods

This study investigates the influence of the eye-camera location associated with the accuracy and precision of interpolation-based eye-tracking methods. Several factors can negatively influence gaze estimation methods when building a commercial or off-the-shelf eye tracker device, including the eye-camera location in uncalibrated setups. Our experiments show that the eye-camera location combined with the non-coplanarity of the eye plane deforms the eye feature distribution when the eye-camera is far from the eye’s optical axis. This paper proposes geometric transformation methods to reshape the eye feature distribution based on the virtual alignment of the eye-camera in the center of the eye’s optical axis. The data analysis uses eye-tracking data from a simulated environment and an experiment with 83 volunteer participants (55 males and 28 females). We evaluate the improvements achieved with the proposed methods using Gaussian analysis, which defines a range for high-accuracy gaze estimation between −0.5∘ and 0.5∘. Compared to traditional polynomial-based and homography-based gaze estimation methods, the proposed methods increase the number of gaze estimations in the high-accuracy range.
Attentional Social Recommendation System with Graph Convolutional Network

As an effective deep representation learning technique for graph, graph convolutional network (GCN) has recently been widely applied to obtain better embedding of vertex. The existing studies have successfully explored user-item interaction via GCN for recommendation task and proved its effectiveness. We argue that a significant limitation of these methods is that social relation, which has been proven to impose positive effects for recommendation in many loss optimization models, has received relatively little scrutiny in GCN. Thus, the resultant embeddings are insufficient to model the potential social propagation effect. In our work, we propose to obtain embeddings by using the neighborhood propagation mechanism on two coupled graph neural networks, i.e., user-item interaction graph and social relation graph, which is capable of capturing the interplay between the item taste of user and user connection. In particular, to address the challenge that different factors on neighborhood propagation process make different contributions for the embedding, we develop a new social recommendation framework with hierarchical attention(i.e., neighbor-level and graph-level attention) Attentional Social Recommendation system (ASR). This allows much flexibility for adaptively acquiring relative importance for different factors. Extensive experiment on two real-world datasets not only show the superior performance of our proposed model over the baselines, but also demonstrate the effectiveness of simultaneous neighborhood propagation on two graphs.
“It's okay, honey… shhh…” - The Media Equation and Computers-Are-Social-Actors-Hypothesis in Acute Care

Technology is indispensable in acute care and is seen primarily as a tool. However, media equation research shows that people also respond socially and naturally to machines as if they were other people. Using qualitative data from participant observations (130 hours) and retrospective interviews (N = 9), we show that technology in acute care settings also elicits reactions as one would typically exhibit towards other people. Based on existing media equation research, we address four aspects: source orientation, specialization, and the computers-are-social-actors hypothesis. Finally, we draw conclusions for qualitative data collection, design, and evaluation of technology in acute care, and discuss how to support human-machine teamwork.
ESIT: An Enhanced Lightweight Algorithm for Secure Internet of Things

With the increasing use of sensors and intelligent devices, Internet of Things (IoT) becomes an important area of research to establish connectivity among connected devices. Traditional algorithms used for encryption are found to be highly complex and with higher number of rounds for encryption which is computationally expensive. In an IoT network, communicating nodes adapt fewer complex algorithms for maintaining security. Hence, lightweight security algorithms are used for this purpose. In this paper, we have proposed a method for encryption and decryption process called Enhanced Secure IoT (ESIT) of an image using shift (<< or > >) and bitwise binary modulo 2 (+2) operation for data transmission. It is a block cipher that accepts a 64-bit key which performs Left shift (<<) and (+2) function for encryption, and right shift (>>) and (+2) decryption. It is the normal bitwise left and right shift by removing the sequence of q-bits from the left and right side, respectively. The performance of the proposed method is established by correlation, entropy, and image histogram. Experimental evaluation on four available images clearly shows the advantages of the given approach.
Nearest neighbors algorithm and genetic‐based collaborative filtering

Abstract Conventional recommender systems often utilize similarity formulas to identify similarities between active users and others to predict the rating of the unseen items. Existing optimization algorithms seek to find the weights and coefficients affecting these similarities. Our proposed method, implemented in R in the GACFF package, shifts away from this view and directly uses the continuous genetic algorithm to find optimal similarities in big data (e.g., Movielens 1M and Netflix datasets) to improve the performance of user‐based collaborative filtering recommendation systems. First, by identifying the users who are the nearest neighbors along with their number, the number of genes in a chromosome is determined. Each gene represents the similarity between a neighboring user and an active user. This genetic algorithm is independent of the size of the data. Our method provides optimal solutions more quickly by estimating the starting points. Moreover, the genetic metric provides better results and recommendations than previous ones in terms of runtime and quality measures (i.e., mean absolute error, coverage, precision, and recall).
Sequential Data Imputation with Evolving Generative Adversarial Networks

Data imputation is an important and widely researched problem in data analysis with applications to a variety of problem domains. Existing literature explores different methods to estimate missing values in a dataset. In this paper, we analyze the task of imputing multiple incomplete numerical datasets that arrive sequentially, each with an overlapping yet distinguishable set of features. We propose a novel procedure based on the Generative Adversarial Network (GAN) architecture, called EvoGAN, which uses transfer learning to build a unified model to perform imputation for all datasets in the sequence. Our methods exploit overlap between datasets and utilize the knowledge gathered from previous rounds. We validate our model on several sequences of datasets and find compelling results for compression rate and acceleration which outperform the state-of-the-art imputation models that are based on an input of a single dataset.
Consensus Analysis of UAV Swarm Cooperative Situation Awareness

UAV swarm cooperative situation awareness (SA) and situation sharing is the basis of swarm cooperative decision and control. Swarm cooperative SA consensus is an important factor affecting swarm consistent decision-making. The related studies of consensus analysis do not consider the levels of SA and the uncertainty of situation information carefully, which is not very suitable for complex and antagonistic mission environments. Combined with the Endsley’s SA three-level model, this paper further proposes UAV swarm cooperative SA consensus three-level model, i.e. situation perception consensus, situation comprehension consensus and situation prediction consensus. The definitions and relationships of the SA consensus three-level model are carefully clarified and analysed. Then, the calculation methods of situation perception consensus and situation prediction consensus are described. The reaching process of situation comprehension consensus with a new feedback mechanism is proposed. A case study demonstrates the reaching process of situation comprehension consensus with the proposed feedback mechanism is efficient.
Machine Learning Based Detection and Classification of Heart Abnormalities

Various types of heart abnormalities require various parameters of consideration for effective detection, analysis, processing and classification of heart abnormalities. Like any other disease, prevention is the key to avoid heart diseases. Heart abnormalities are very common not only in India but all over the world, but with better understanding of different symptoms of heart problems, proper diagnosis and prognosis is possible, so that early and optimal treatment is initiated. Particularly in these Pandemic situations of COVID 19 which led to more stress and as a consequence many heart related issues. Deep learning is also vastly employed technique in recent researches for classification of heart images and biomedical images in general. Neural network and support vector machine (SVM) algorithms are also frequently used in classification because of higher accuracy in results. The use of machine learning resulted in improved accuracy and reduced variability in comparison to manual quantification of echocardiographic parameters. A hybrid algorithm (Image enhancement and denoising both carried out in preprocessing task) is developed in this article which can efficiently and effectively classify the heart abnormalities by detecting whether the image is normal or abnormal, if abnormal whether mild stage or critical stage.
Jitter: Random Jittering Loss Function

Regularization plays a vital role in machine learning optimization. One novel regularization method called flooding makes the training loss fluctuate around the flooding level. It intends to make the model continue to “random walk” until it comes to a flat loss landscape to enhance generalization. However, the hyper-parameter flooding level of the flooding method fails to be selected properly and uniformly. We propose a novel method called Jitter to improve it. Jitter is essentially a kind of random loss function. Before training, we randomly sample the “Jitter Point” from a specific probability distribution. The flooding level should be replaced by Jitter point to obtain a new target function and train the model accordingly. As Jitter point acting as a random factor, we actually add some randomness to the loss function, which is consistent with the fact that there exists innumerable random behaviors in the learning process of the machine learning model and is supposed to make the model more robust. In addition, Jitter performs “random walk” randomly which divides the loss curve into small intervals and then flipping them over, ideally making the loss curve much flatter and enhancing generalization ability. Moreover, Jitter can be a domain-, task-, and model-independent regularization method and train the model effectively after the training error reduces to zero. Our experimental results show that Jitter method can improve model performance more significantly than the previous flooding method and make the test loss curve descend twice.
Task Allocation in Multi-Agent Systems with Grammar-Based Evolution

This paper presents a grammar-based evolutionary model to facilitate autonomous emergence of task allocation for intelligent multi-agent systems. The approach adopts a context-free grammar to determine the behaviour rule syntax. This allows for flexibility in evolving task allocation under multiple and dynamic constraints without manual rule design and parameter tuning. Experimental evaluations conducted with a target discovery simulation illustrate that the grammar-based model performs successfully in both dynamic and non-dynamic conditions. A statistically significant performance improvement is shown compared to an algorithm developed with the broadcast of local eligibility mechanism and a genetic programming mechanism. Grammatical evolution can achieve near-optimal solutions under restrictions applied on the number of agents, targets and the time allowed. Further, analysis of the evolved rule structures shows that grammatical evolution can identify less complex rule structures for behaviours while maintaining the expected level of performance. The results infer that the proposed model is a promising alternative for dynamic task allocation with human interactions in complex real-world domains.
Image synthesis via adversarial geometric consistency pursuit

Realistic image synthesis is a class of vision problems where the goal is to synthesize new images by fusing a source image and a target image. It is a challenging problem due to the visual gap between the two images, including geometry and appearance. To synthetically address the geometric distortion and appearance realism, we propose a novel method, the adversarial geometric consistency pursuit model (AGCP), which explicitly allows seamless image synthesis. The proposed method takes geometric correction and appearance harmonization into account, handling the relative scaling, spatial layout, color, viewpoint, and distortion transformation to generate a realistic composite image. Moreover, we also propose a joint geometric consistency pursuit loss that handles the geometric consistency and enhances the network to generalize better for different scales of source images. Our comparative evaluation demonstrates the effectiveness of the proposed method in the aforementioned challenging cases.