Reviewerly/exHarmony huggingface dataset:
GPGC: Genetic programming for automatic clustering using a flexible non-hyper-spherical graph-based approach

© 2017 ACM. Genetic programming (GP) has been shown to be very effective for performing data mining tasks. Despite this, it has seen relatively little use in clustering. In this work, we introduce a new GP approach for performing graph-based (GPGC) non-hyper-spherical clustering where the number of clusters is not required to be set in advance. The proposed GPGC approach is compared with a number of well known methods on a large number of data sets with a wide variety of shapes and sizes. Our results show that GPGC is the most generalisable of the tested methods, achieving good performance across all datasets. GPGC significantly outperforms all existing methods on the hardest ellipsoidal datasets, without needing the user to pre-define the number of clusters. To our knowledge, this is the first work which proposes using GP for graph-based clustering.
Demystification of Bilingual Optical Character Recognition System for Devanagari and English Scripts

Research is deliberately going on in the field of pattern recognition. New ideas are developed and implemented in this field throughout the globe. Optical Character Recognition (OCR) is one of the inseparable applications of Pattern Recognition. Though extensive research is already reported in this field, but multilingual Optical Character Recognition is the most challenging aspect which is still, the need of the hour. Myriads of researchers are digging the information to gather the best solutions for the recognition purpose. In this research paper, we are purposing the steps for the recognition of Devanagari and English scripts simultaneously occurring in the documents. A new approach of segmentation and splitting the characters of both the scripts is also introduced for the benefits of researchers. Most commonly in the documents containing English and Devanagari scripts, English characters are already separated, the challenge is to separate the Devanagari characters. Algorithm to implement the challenging aspect to segment the Devanagari and Roman scripts simultaneously is also implemented in the present paper.
Back Cover Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 9 No. 2 Juli 2020

Back Cover Journal
An effective feature learning approach using genetic programming with image descriptors for image classification

© 2005-2012 IEEE. Being able to extract effective features from different images is very important for image classification, but it is challenging due to high variations across images. By integrating existing well-developed feature descriptors into learning algorithms, it is possible to automatically extract informative high-level features for image classification. As a learning algorithm with a flexible representation and good global search ability, genetic programming can achieve this. In this paper, a new genetic programming-based feature learning approach is developed to automatically select and combine five existing well-developed descriptors to extract high-level features for image classification. The new approach can automatically learn various numbers of global and/or local features from different types of images. The results show that the new approach achieves significantly better classification performance in almost all the comparisons on eight data sets of varying difficulty. Further analysis reveals the effectiveness of the new approach to finding the most effective feature descriptors or combinations of them to extract discriminative features for different classification tasks.
Session details: Theme: AI and agents: HCI - Smart human computer interaction track

As the track chairs of the Smart Human Computer Interaction, we would like to publish the latest, innovative, and outstanding research results in the ACM SAC 2019. This track was called for papers and participation in the 34th ACM/SIGAPP Symposium on Applied Computing held in Limassol, Cyprus, on April 8 -- 12, 2019. The major components of the track were human and computer interaction.
VSD blowers bring energy savings

At a Scottish wastewater treatment works, lobe air blowers in its biological air flooded filter required constant attention and maintenance. Replacement units in the shape of VSD, low-pressure, rotary screw blowers from Atlas Copco provided the solution.
Load Balancing Technique with an Efficient Method

There are a huge number of nodes connected to web computing to offer various types of web services to provide cloud clients. Limited numbers of nodes connected to cloud computing have to execute more than a thousand or a million tasks at the same time. So it is not so simple to execute all tasks at the same particular time. Some nodes execute all tasks, so there is a need to balance all the tasks or loads at a time. Load balance minimiz-es the completion time and executes all the tasks in a particular way.There is no possibility to keep an equal number of servers in cloud computing to execute an equal number of tasks. Tasks that are to be performed in cloud computing would be more than the connected servers. Limited servers have to perform a great number of tasks.We propose a task scheduling algorithm where few nodes perform the jobs, where jobs are more than the nodes and balance all loads to the available nodes to make the best use of the quality of services with load balancing.
On Reducing the Number of Multiplications in RNS-based CNN Accelerators

In this paper, a method to reduce the number of multiplications in Convolutional Neural Networks (CNNs) by exploiting the properties of the Residue Number System (RNS) is proposed. RNS decomposes the elementary computations into a number of small bit-width, independent channels, which can be processed in parallel. Naturally, due to the small dynamic range of each RNS channel, the number of common factors inside the weight kernels during a convolution is increased. By identifying these common factors and by rearranging the order of computations to perform first the additions of the input feature-map terms that correspond to the same factors, the number of multiplications can be reduced up to 97 %, for state-of-the-art CNN models. The remaining multiplications are also simplified, as they are implemented through shift-add operations or fixed-operand multipliers. ASIC implementations of the proposed Processing Element (PE) architecture show a speedup of up to 2.67× and 1.64× compared to the binary and conventional RNS counterparts, respectively. Compared to a conventional RNS PE implementation, the proposed method also leads to a 20% reduction in area and 16% reduction in power consumption.
Implementation of Microservices Architecture in Learning Management System E-Course Using Web Service Method

&#x0D; &#x0D; &#x0D; Building a learning system using the Learning Management System is already too much but there are many problems that make people think about using it because it is difficult to maintain the system, so to build a system takes a lot of time because the basis for developing the system is monolith-based and difficult to develop if the system development team grows or change. Learning Management System needs to be implemented for easy access to learning because online learning is very important to be a good alternative. From these problems led to the idea to create a Learning Management System in which it can solve services and communicate between services to facilitate efficient development. This study aims to apply the Web Service Implementation Methodology used in building a Learning Management System for an E-Course that facilitates online learning by implementing web services. To perform inter-service solving the methodology used in making this application is the web service method which is implemented on the microservices architecture. Programming languages for differentiating services are PHP and JavaScript. For the user interface, ReactJS is used as the API data manager for each service and the database uses MySQL. This application will later be used as a medium to facilitate online learning, there are members (users) as student actors and admins to manage mentor and course data that will be displayed.&#x0D; &#x0D; &#x0D;
Cross-Domain Recommendation Via User-Clustering and Multidimensional Information Fusion

Recently, recommendation systems have been widely usedin online business scenarios, which can improve the online experience by learning the user or item characteristics to predict the user’s future behavior and to realize precision marketing. However, data sparsity and cold-start problems limit the performance of recommendation systems in some emerging fields. Thus, cross-domain recommendation has been proposed to handle the abovementioned problems. Nonetheless, many cross-domain recommendations only consider modeling a single user’s representation and ignore user-group information (this group has similar behavior and interests). Additionally, most studies are based on matrix factorization for generating embeddings, which results in a weak generalization ability of user latent features. In this paper, we propose a novel cross-domain recommendation model via <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">U</b> ser- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> lustering and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> ultidimensional information <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</b> usion (UCMF) that attempts to enhance user representation learning in a data sparsity scenario for accurate recommendation. In addition, we consider a user’s individual information and cross-domain feature information. A novel multidimensional information fusion is proposed to guarantee the robustness of the user features. In particular, we apply a graph neural network to learn the user-group features, which can effectively save the correlation among users’ information and guarantee feature performance. In other words, the Wasserstein autoencoder is utilized to learn the cross-domain user features, which can guarantee the consistency of user features from different domains. Experiments conducted on real-world datasets empirically demonstrate that our proposed method outperforms the state-of-the-art methods in cross-domain recommendation.
Zero-shot Recognition with Image Attributes Generation using Hierarchical Coupled Dictionary Learning

Zero-shot learning (ZSL) aims to recognize images from unseen (novel) classes with the training images from seen classes. The attributes of each class is exploited as auxiliary semantic information. Recently most ZSL approaches focus on learning visual-semantic embeddings to transfer knowledge from the seen classes to the unseen classes. However, few works study whether the auxiliary semantic information in the class-level is extensive enough or not for the ZSL task. To tackle such problem, we propose a hierarchical coupled dictionary learning (HCDL) approach to hierarchically align the visual-semantic structures in both the class-level and the image-level. Firstly, the class-level coupled dictionary is trained to establish a basic connection between visual space and semantic space. Then, the image attributes are generated based on the basic connection. Finally, the fine-grained information can be embedded by training the image-level coupled dictionary. Zero-shot recognition is performed in multiple spaces by searching the nearest neighbor class of the unseen image. Experiments on two widely used benchmark datasets show the effectiveness of the proposed approach.
Improving Dialogue State Tracking by Joint Slot Modeling

Dialogue state tracking models play an important role in a task-oriented dialogue system.However, most of them model the slot types conditionally independently given the input.We discover that it may cause the model to be confused by slot types that share the same data type.To mitigate this issue, we propose TripPy-MRF and TripPy-LSTM that models the slots jointly.Our results show that they are able to alleviate the confusion mentioned above, and they push the state-of-the-art on dataset Multi-WoZ 2.1 from 58.7 to 61.3.Our implementation is available at https://github.com/CTinRay/Trippy-Joint.
Item correlation modeling in interaction sequence for graph convolutional session recommendation

Session-based recommendation aims to predict the user's follow-up behavior based on the user's short-term behavior in the session and is mostly used in scenarios where the user visits the website anonymously. The key problem of session-based recommendation is to model collaborative relationships between items to improve the recommendation performance. Previous methods primarily focused on discovering the interaction sequence of items to learn correlations between items and only explored the information of adjacent interactive items without taking into account historical interactive information. Actually, if two items share the same historical interaction information, they can be considered to have strong relationships. For example, in a session where the interaction sequences of two items are $I_1$-$I_2$-$I_3$ and $I_1$-$I_2$-$I_4$, the historical interaction information of item $I_3$ and item $I_4$ are both $I_1$-$I_2$. Thus, item $I_3$ and item $I_4$ can be deemed as similar Toward this end, this paper proposes a new session-based recommendation model named IHGCN to jointly explore the adjacent interactive information and the historical interactive information by analyzing the interaction sequence of items in the session. To learn the item feature from the interactive information, the proposed method introduces the graph structure to model correlations between items and then uses graph convolutional networks to abstract the item features from the item correlation graph The proposed IHGCN method introduces a fine-grained attention mechanism in the feature dimension level to discover the global preference of users and then integrate the local preference of users. Based on the learned item features and user preference features, the final recommendation results are obtained. Experiments conducted on three public datasets show the superiority of the proposed method to the state-of-the-art methods.
Three-Dimensional Obstacle Avoidance Strategy for Fixed-Wing UAVs Based on Quaternion Method

This work provides a generalization of the three-dimensional velocity obstacle (VO) collision avoidance strategy for nonlinear second-order underactuated systems in three-dimensional dynamic uncertain environments. A hierarchical architecture is exploited to deal with conflicting multiple subtasks, which are defined as several rotations and are parameterized by quaternions. An improved VO method considering the kinodynamic constraints of a class of fixed-wing unmanned aerial vehicles (UAV) is proposed to implement the motion planning. The position error and velocity error can be mapped onto one desired axis so that, only relying on an engine, UAVs can achieve the goal of point tracking without collision. Additionally, the performance of the closed-loop system is demonstrated through a series of simulations performed in a three-dimensional manner.
Data Mining of Regional Economic Analysis Based on Mobile Sensor Network Technology

With the continuous development of regional economy, the difference of regional economy has also aroused the attention of all walks of life. Due to the limitations of the traditional research methods, the research results are relatively simple and unable to conduct a more comprehensive analysis. The traditional methods include the following: (1) analyze the evolution of regional logistics based on the location Gini coefficient and location quotient of GIS, and reflect the situation of industrial agglomeration from the annual change curve of the location Gini coefficient; (2) use SPSS12.0 software to perform multivariate or event factors, and analyze and calculate the factor score to sum up several principal component factors; and (3) the production function analysis method is used to measure the economies of scale and agglomeration. As an extension, the relationship between the estimated total output and the agglomeration index of the factor input to measure the uniform state of the industrial distribution department is an effective measurement method for the agglomeration economy. In order to promote the sustainable development of regional economy, this paper analyzes the regional economy comprehensively based on the emerging mobile sensor network technology and data mining technology. Firstly, this paper analyzes the location technology of mobile sensor networks based on sequential Monte Carlo, selects the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>C</mi> </math> -means clustering method which is suitable for economic large-sample clustering analysis, and constructs a complete data mining model. Then, the model is used to analyze the economic, social, natural, and educational science and technology indicators of a certain region from 2015 to 2019. The results show that the first principal component weight of economic indicators is the highest proportion of fiscal revenue, which is 0.986. This shows that the role of fiscal revenue in economic indicators is greater. The main index of urban consumption is 72.0, which is the highest. This shows that the population growth rate and the average consumption of urban households in social indicators play a greater role. The first principal component of natural index has the highest weight of pollution emission, which is 0.47, while the second principal component has the highest weight of total energy consumption, which is 0.48. This shows that the pollution emissions and total energy consumption in the natural indicators play a greater role. In the educational science and technology index, the first principal component weight is the highest, which is 0.61. This shows that the education funds play an important role in educational science and technology indicators. Therefore, the data mining model based on mobile sensor network technology can comprehensively and accurately analyze various indicators of regional economy.
Towards Enhancing the Performance of Parallel FP-Growth on Spark

Frequent itemset mining (FIM) is a crucial tool for identifying hidden patterns in information.FP-Growth is an FIM algorithm used to find associations.When the data size increases, the execution of FIM algorithms on a single machine suffers from computational problems, such as memory and time consumption.For these reasons, parallel and distributed processing on platforms such as Spark is essential.The parallel frequent pattern (PFP) is the implementation of FP-Growth in Spark.The main problem with PFP is that it does not consider the load balancing between cluster units.This research proposes an enhanced balanced parallel frequent pattern ''EBPFP'' algorithm to enhance and balance the PFP.The proposed algorithm (EBPFP) proposes two ideas.First, a strategy for load balancing between groups is proposed to ensure that the items are evenly divided between the nodes, and the cluster resources are used more effectively.Second, the improved conditional pattern base (ICPB) method aims to remove infrequent items from the conditional pattern base before constructing local FP-Trees.The experimental results show that the proposed EBPFP algorithm outperforms PFP, and the difference in running time between EBPFP and PFP was 21.56% and 39.72%, respectively.
Optimization of College English Classroom Teaching Efficiency by Deep Learning SDD Algorithm

In order to improve the teaching efficiency of English teachers in classroom teaching, the target detection algorithm in deep learning and the monitoring information from teachers are used, the target detection algorithm of deep learning Single Shot MultiBox Detector (SSD) is optimized, and the optimized Mobilenet-Single Shot MultiBox Detector (Mobilenet-SSD) is designed. After analyzing the Mobilenet-SSD algorithm, it is recognized that the algorithm has the shortcomings of large amount of basic network parameters and poor small target detection. The deficiencies are optimized in the following partThrough related experiments of student behaviour analysis, the average detection accuracy of the optimized algorithm reached 82.13%, and the detection speed reached 23.5 fps (frames per second). Through experiments, the algorithm has achieved 81.11% in detecting students' writing behaviour. This proves that the proposed algorithm has improved the accuracy of small target recognition without changing the operation speed of the traditional algorithm. The designed algorithm has more advantages in detection accuracy compared with previous detection algorithms. The optimized algorithm improves the detection efficiency of the algorithm, which is beneficial to provide modern technical support for English teachers to understand the learning status of students and has strong practical significance for improving the efficiency of English classroom teaching.
A mutually-exclusive binary cross tagging framework for joint extraction of entities and relations

Joint extraction from unstructured text aims to extract relational triples composed of entity pairs and their relations. However, most existing works fail to process the overlapping issues that occur when the same entities are utilized to generate different relational triples in a sentence. In this work, we propose a mutually exclusive Binary Cross Tagging (BCT) scheme and develop the end-to-end BCT framework to jointly extract overlapping entities and triples. Each token of entities is assigned a mutually exclusive binary tag, and then these tags are cross-matched in all tag sequences to form triples. Our method is compared with other state-of-the-art models in two English public datasets and a large-scale Chinese dataset. Experiments show that our proposed framework achieves encouraging performance in F1 scores for the three datasets investigated. Further detailed analysis demonstrates that our method achieves strong performance overall with three overlapping patterns, especially when the overlapping problem becomes complex.
Improved Objective Functions to Search for 8 × 8 Bijective S-Boxes With Theoretical Resistance Against Power Attacks Under Hamming Leakage Models

Many research focuses on finding S-boxes with good cryptographic properties applying a heuristic method and a balanced, objective function. The design of S-boxes with theoretical resistance against Side-Channel Attacks by power consumption is addressed with properties defined under one of these two models: the Hamming Distance leakage model and the Hamming Weight leakage model. As far as we know, a balanced search criterion that considers properties under both, at the same time, remains an open problem. We define two new optimal objective functions that can be used to obtain S-boxes with good cryptographic properties values, keeping high theoretical resistance for the two leakage models; we encourage using at least one of our objective functions. We apply a Hill Climbing heuristic method over the S-box’s space to measure which objective function is better and to compare the obtained S-boxes with the S-boxes in the actual literature. We also confirm some key relationships between the properties and which property is more suitable to be used.