Reviewerly/exHarmony huggingface dataset:
Confluence and Commutation for Nominal Rewriting Systems with Atom-Variables

Nominal rewriting was introduced as an extension of first-order term rewriting by a binding mechanism based on the nominal approach. Recently, a new format of nominal rewriting has been introduced where rewrite rules are defined with atom-variables rather than atoms. In this paper, we investigate the difference between the new format and the original nominal rewriting, and prove confluence and commutation for some classes of rewriting systems whose rewrite rules have no proper overlaps which are computed using nominal unification with atom-variables. The properties we prove are expected to be used in a form of program transformation that is realised as an equivalence transformation of rewriting systems.
Mask R-CNN for Indonesian Shadow Puppet Recognition and Classification

Abstract Region-based Convolutional Neural Networks (R-CNN) have achieved remarkable achievements in object recognition and detection. In this paper, Mask R-CNN is introduced to detect and recognize Indonesian Shadow Puppet patterns. Indonesian Shadow Puppet (Wayang) is one of the traditional Indonesian arts that depict stories, one of the stories is the Mahabharata. Our aim for conducting this research is to protect Indonesian Puppets (Wayang) from extinction through systemic pattern recognition. The algorithm used in this study is Mask R-CNN. Mask R-CNN training to recognize Indonesian Shadow Puppet requires many labels that are difficult to obtain. Therefore, the authors carried out data augmentation to add datasets to improve training. Furthermore, testing is done using several learning rates. This test is done using a Cloud GPU. The results of the tests show that the accuracy of the training obtained is 92.04% and the accuracy of validation with the learning rate value set is 0.0001. Testing the model in this study successfully tested the puppet pattern.
Exploiting Various Word Embedding Models for Query Expansion in Microblog

Microblogs, especially Twitter, make it easier to communicate with others in a real-time manner and is treated as a valuable information source. With the increasing amount of tweets, it would be fascinating to be able to extract essential information out of those diverse tweets. However, due to the length constraint in Twitter, users typically use unfamiliar short forms, ambiguous expressions, Twitter-specific syntaxes, and URLs to convey their brief thoughts. All of these aspects incur the severe vocabulary mismatch problem and make it difficult to perform effective information retrieval (IR) on Twitter. In this paper, we propose a query expansion method that ameliorates the initial queries with expansion terms which reflects the user’s intent effectively. To select the effective candidate expansion terms, we exploit the various word embedding models including Word2Vec, GloVe, and fastText that are trained with the different local and external corpus. Our ensemble word embedding approach helps to extract the effective contextual features of terms. Next, we ranked the candidate terms based on the mean cosine similarity score of each query-term pair and use the top-ranked terms to augment the initial query. We have performed the experiments on TREC Microblog 2011-2012 test sets covering TREC Tweets2011 corpora. Experimental results exhibit the efficacy of our query expansion method over the other competitive approaches.
Prediction of remaining time on site for e‐commerce users: A SOM and long short‐term memory study

Abstract With the development of information technology, online transactions and e‐commerce are gradually replacing conventional consumption patterns. To obtain a competitive advantage, industries proactively engage in digital transformations and the management of e‐commerce platforms. Faced with changes in market patterns, e‐commerce channels and online advertising firms hope to extend users' website browsing duration / time on site to enhance the effects of product promotion and the likelihood of advertisement clicks. The greatest challenge in predicting time on site is that clickstream data are not mutually independent, and short‐, mid‐, and long‐term data may intervene in a time series. Such timing dependence increases difficulty of capturing or learning the characteristics of website users for ordinary prediction models and leads to confusion and deviation during model construction. Accordingly, this study proposed a prediction method integrating self‐organizing map (SOM) and long short‐term memory (LSTM). The SOM method was initially applied to categorize website members into groups based on similarities in browsing behavior, and the LSTM prediction model was subsequently developed using the webpage browsing data of each group. The performance of the proposed method is evaluated by comparing the prediction with the results of three competing approaches (SOM with support vector regression, SOM with multilayer perceptron, and single LSTM) on the clickstream data provided by a leading online retailer specializing in selling skin care and cosmetics products in Taiwan. The Wilcoxon signed‐rank test validated the proposed SOM‐LSTM model outperforms competing approaches in remaining time‐on‐site prediction. This study serves as a first attempt to systematically predict remaining time on site for e‐commerce users in terms of empirically verifying a hybrid approach which integrates SOM and LSTM techniques.
Multilevel Image Segmentation using Hybrid Grasshopper Optimization and k-means Algorithm

Image Segmentation has been considered as challenging task in image processing field. In this paper we proposed a new method for image segmentation purpose. Our proposed method is based on combining Grasshopper Optimization Algorithm (GOA) and k-means clustering. Grasshopper Optimization Algorithm (GOA), inspired by nature, divides the search process into two parts of exploration and utilization. The proposed algorithm in terms of mathematics models the behavior of Grasshopper groups in nature for solving the problems of optimization. We evaluate the proposed algorithm function based on eight-image segmentation and compare the obtained results of the tests of PSNR scale and SSIM measurement with four moth-flame whale algorithms by harmony search and social spider. The results indicate that the proposed algorithm with a simple threshold gives us a more optimized response regarding other state of arts in terms of both objective and subjective criteria.
Sure Based Convolutional Neural Networks for Hyperspectral Image Denoising

This paper addresses the hyperspectral image (HSI) denoising problem by using Stein's unbiased risk estimate (SURE) based convolutional neural network (CNN). Conventional deep learning denoising approaches often use supervised methods that minimize a mean-squared error (MSE) by training on noisy-clean image pairs. In contrast, our proposed CNN-based denoiser is unsupervised and only makes use of noisy images. The method uses SURE, which is an unbiased estimator of the MSE, that does not require any information about the clean image. Therefore minimization of the SURE loss function can accurately estimate the clean image only from noisy observation. Experimental results on both simulated and real hyperspectral datasets show that our proposed method outperforms competitive HSI denoising methods.
Method for detection of unsafe actions in power field based on edge computing architecture

Abstract Due to the high risk factors in the electric power industry, the safety of power system can be improved by using the surveillance system to predict and warn the operators’ nonstandard and unsafe actions in real time. In this paper, aiming at the real-time and accuracy requirements in video intelligent surveillance, a method based on edge computing architecture is proposed to judge unsafe actions of electric power operations in time. In this method, the service of unsafe actions judgment is deployed to the edge cloud, which improves the real-time performance. In order to identify the action being executed, the end-to-end action recognition model proposed in this paper uses the Temporal Convolutional Neural Network (TCN) to extract local temporal features and a Gate Recurrent Unit (GRU) layer to extract global temporal features, which increases the accuracy of action fragment recognition. The result of action recognition is combined with the result of equipment target recognition based on the yolov3 model, and the classification rule is used to determine whether the current action is safe. Experiments show that the proposed method has better real-time performance, and the proposed action cognition is verified on the MSRAction Dataset, which improves the recognition accuracy of action segments. At the same time, the judgment results of unsafe actions also prove the effectiveness of the proposed method.
Optimization of cloud load balancing using fitness function and duopoly theory

Purpose Current industrial scenario is largely dependent on cloud computing paradigms. On-demand services provided by cloud data centre are paid as per use. Hence, it is very important to make use of the allocated resources to the maximum. The resource utilization is highly dependent on the allocation of resources to the incoming request. The allocation of requests is done with respect to the physical machines present in the datacenter. While allocating the tasks to these physical machines, it needs to be allocated in such a way that no physical machine is underutilized or over loaded. To make sure of this, optimal load balancing is very important. Design/methodology/approach The paper proposes an algorithm which makes use of the fitness functions and duopoly game theory to allocate the tasks to the physical machines which can handle the resource requirement of the incoming tasks. The major focus of the proposed work is to optimize the load balancing in a datacenter. When optimization happens, none of the physical machine is neither overloaded nor under-utilized, hence resulting in efficient utilization of the resources. Findings The performance of the proposed algorithm is compared with different existing load balancing algorithms such as round-robin load (RR) ant colony optimization (ACO), artificial bee colony (ABC) with respect to the selected parameters response time, virtual machine migrations, host shut down and energy consumption. All the four parameters gave a positive result when the algorithm is simulated. Originality/value The contribution of this paper is towards the domain of cloud load balancing. The paper is proposing a novel approach to optimize the cloud load balancing process. The results obtained show that response time, virtual machine migrations, host shut down and energy consumption are reduced in comparison to few of the existing algorithms selected for the study. The proposed algorithm based on the duopoly function and fitness function brings in an optimized performance compared to the four algorithms analysed.
Active Improvement of Control Policies with Bayesian Gaussian Mixture Model

Learning from demonstration (LfD) is an intuitive framework allowing non-expert users to easily (re-)program robots. However, the quality and quantity of demonstrations have a great influence on the generalization performances of LfD approaches. In this paper, we introduce a novel active learning framework in order to improve the generalization capabilities of control policies. The proposed approach is based on the epistemic uncertainties of Bayesian Gaussian mixture models (BGMMs). We determine the new query point location by optimizing a closed-form information-density cost based on the quadratic Rényi entropy. Furthermore, to better represent uncertain regions and to avoid local optima problem, we propose to approximate the active learning cost with a Gaussian mixture model (GMM). We demonstrate our active learning framework in the context of a reaching task in a cluttered environment with an illustrative toy example and a real experiment with a Panda robot.
Estimate Depth Information from Monocular Infrared Images Based on Deep Learning

In the field of automatic driving, vision-based driving assistance system(V-DAS) is a research direction. Aiming at the demand of V-DAS for low visibility environment perception , we propose a depth estimation method based on monocular infrared images. Our method uses an end-to-end self-supervised deep learning framework, and uses the reprojection relationship between monocular video frames to construct the loss function, which makes it easy to collect dataset. In order to mitigate the influence of infrared noise, we adopt the minimum reprojection loss method. To ensure the neural network can learn infrared features better, we use transfer learning from RGB image dataset first. Qualitative experiments on FLIR datasets show that the proposed method can obtain pixel-lev el dense depth from monocular infrared images. Experiments on real roads show that the proposed method can effectively perceive the depth information of the target in the night, and the absolute error is 13.2% within 15m. This can meet the requirements of collision avoidance in most emergency situations during automatic driving.
HOW BEHAVIOR OF USERS IMPACTS THE SUCCESS OF ONLINE Q&amp;A COMMUNITIES

What makes Question &amp; Answer (Q&amp;A) communities productive? In this paper, we look into how the diversity of behavioral types of agents impacts the performance of Q&amp;A communities using different performance metrics. We do this by developing an agent-based model informed by insights from previous studies on Q&amp;A communities. By analyzing the different strategies for how questions are selected to answer, we find that there are mixtures of strategies leading to the best outcomes for different performance conditions. Particularly, Q&amp;A communities that encourage participants to focus on answering the new questions reach the best performance in answering the questions, creating the long term value, and improving the competence of solving difficulty. In conclusion, we find that the current strategies of question selection on Stack Overflow are in line with the high performance of producing public benefit from the collective attention available.
Application platform for e-reservation system in traditional show

Abstract The purpose of conducting this research is an effort to maintain and improve the preservation of Indonesian traditional arts using internet marketplace technology. The method used was literature study and field observations to Sekar Saluyu Art Studio as a provider of traditional arts services. The results of this study explained how the application-based marketplace role, which is named by Kenda application, has improved the economy of Indonesian traditional arts, helped develop, and maintain the preservation of traditional arts by using internet technology platforms to compete against foreign cultures. Advances in information technology are a reason to be able to preserve Indonesian traditional arts so that they are not left behind. For this reason, this effort was built to solve the problems of Indonesian traditional arts in the current information era. Therefore, Kenda App Platform was used to provide solutions to these problems.
RODA: A Fast Outlier Detection Algorithm Supporting Multi-Queries

Outlier detection is an important task in the field of big data analysis. The technology has been extensively used in network security, sensor data analysis, public health and so on. In an outlier detection system, with the continuous expansion of upper-layer applications, a system needs to process a large number of query requests in a very short time, which places high requirements on the timeliness of outlier detection algorithms. To solve this problem, in this paper, an efficient algorithm, R-tree based Outlier Detection Algorithm (RODA), is proposed, which can effectively support single query and multiple query processing. For single query processing, we first extended the R-tree index and proposed a new outlier estimation method. Using the techniques above, the algorithm greatly reduces the retrieval space by preferentially scanning data points with high outlier-degrees. For multiple query processing, the algorithm deeply analyzes the sharing mechanism between multiple queries in order to handle multiple detection tasks within one processing. Finally, experiment results show that the RODA proposed in this paper has improved operating efficiency, and has good applicability and practical significance.
Kinesthetic Empathy in Remote Interactive Performance: Research into Platforms and Strategies for Performing Online

In interactive performance, kinesthetic empathy refers to the ability of performers to read, decode, and react to one another's physical input. To develop kinesthetic empathy, performers rely on sonic, visual, and other sensory cues. Remote and interactive performance oftentimes limits these cues due to deficiencies of the virtual environment. In this case, performers must develop connections between alternate sensory modes to achieve kinesthetic empathy. This paper explores alternative systems for remote performance and investigates the ways in which human players creatively exploit these platforms as well as defining pre-requisite sensory connections needed to achieve kinesthetic empathy between remote participants. We present three examples of technologies and performance techniques used to achieve this connection. We then present a new system modified for remote performance and propose a strategy for demonstrating it to peers in order to discern its effectiveness in facilitating kinesthetic empathy between multiple players as well as players and audience. We use current research data in cognitive psychology as a baseline for our own inquiry and hope our experiences will inspire future research in that field.
ContiMap: Continuous Heatmap for Large Time Series Data

Limited human cognitive load, limited computing resources, and finite display resolutions are the major obstacles for developing interactive visualization systems in large-scale data analysis. Recent technological innovation has significantly improved computing power, such as faster CPUs and GPUs, as well as display resources, including ultra-high-resolution displays and video walls. However, large and complex data is still ahead in the run as we are generating huge amounts of data daily. Our strategy to bridge these gaps is to present the right amount of information through the use of compelling graphics. This paper proposes an approximation algorithm and a web prototype for representing a high-level abstraction of time series based on heatmap designs. Our approach aims to handle a significant amount of time series data arising from various application domains, such as cybersecurity, sensor network, and gene expression analysis.
Deep Gaussian Processes Using Expectation Propagation and Monte Carlo Methods

Deep Gaussian processes (DGPs) are the natural extension of Gaussian processes (GPs) to a multi-layer architecture. DGPs are powerful probabilistic models that have shown better results than standard GPs in terms of generalization performance and prediction uncertainty estimation. Nevertheless, exact inference in DGPs is intractable, making these models hard to train. For this task, current approaches in the literature use approximate inference techniques such as variational inference or approximate expectation propagation. In this work, we present a new method for inference in DGPs using an approximate inference technique based on Monte Carlo methods and the expectation propagation algorithm. Our experiments show that our method scales well to large datasets and that its performance is comparable or better than other state of the art methods. Furthermore, our training method leads to interesting properties in the predictive distribution of the DGP. More precisely, it is able to capture output noise that is dependent on the input and it can generate multimodal predictive distributions. These two properties, which are not shared by other state-of-the-art approximate inference methods for DGPs, are analyzed in detail in our experiments.
A Scoring System to Efficiently Measure Security in Cyber-Physical Systems

The importance of Cyber-Physical Systems (CPS) gains more and more weight in our daily business and private life. Although CPS build the backbone for major trends, like Industry 4.0 and connected vehicles, they also propose many new challenges. One major challenge can be found in achieving a high level of security within such highly connected environments, in which an unpredictable number of heterogeneous systems with often-distinctive characteristics interact with each other. In order to develop high-level security solutions, system designers must eventually know the current level of security of their specification. To this end, security metrics and scoring frameworks are essential, as they quantitatively express security of a given design or system. However, existing solutions may not be able to handle the proposed challenges of CPS, as they mainly focus on one particular system and one specific attack. Therefore, we aim to elaborate a security scoring mechanism, which can efficiently be used in CPS, while considering all essential information. We break down each system within the CPS into its core functional blocks and analyze a variety of attacks in terms of exploitability, scalability of attacks, as well as potential harm to targeted assets. With this approach, we get an overall assessment of security for the whole CPS, as it integrates the security-state of all interacting systems. This allows handling the presented complexity in CPS in a more efficient way, than existing solutions.
Modified Firefly Algorithm and Fuzzy C-Mean Clustering Based Semantic Information Retrieval

As enormous volume of electronic data increased gradually, searching as well as retrieving essential info from the internet is extremely difficult task. Normally, the Information Retrieval (IR) systems present info dependent upon the user’s query keywords. At present, it is insufficient as large volume of online data and it contains less precision as the system takes syntactic level search into consideration. Furthermore, numerous previous search engines utilize a variety of techniques for semantic based document extraction and the relevancy between the documents has been measured using page ranking methods. On the other hand, it contains certain problems with searching time. With the intention of enhancing the query searching time, the research system implemented a Modified Firefly Algorithm (MFA) adapted with Intelligent Ontology and Latent Dirichlet Allocation based Information Retrieval (IOLDAIR) model. In this recommended methodology, the set of web documents, Face book comments and tweets are taken as dataset. By means of utilizing Tokenization process, the dataset pre-processing is carried out. Strong ontology is built dependent upon a lot of info collected by means of referring via diverse websites. Find out the keywords as well as carry out semantic analysis with user query by utilizing ontology matching by means of jaccard similarity. The feature extraction is carried out dependent upon the semantic analysis. After that, by means of Modified Firefly Algorithm (MFA), the ideal features are chosen. With the help of Fuzzy C-Mean (FCM) clustering, the appropriate documents are grouped and rank them. At last by using IOLDAIR model, the appropriate information’s are extracted. The major benefit of the research technique is the raise in relevancy, capability of dealing with big data as well as fast retrieval. The experimentation outcomes prove that the presented method attains improved performance when matched up with the previous system.