Reviewerly/exHarmony huggingface dataset:
LSTM-XGB: A New Deep Learning Model for Human Activity Recognition based on LSTM and XGBoost

Recognition of human activity focuses on identifying various human motions and behaviors using data acquired from multiple types of sensors, a branch of computer science. Considering time series data, deep learning algorithms show promise compared to laborious handcrafted feature extraction methods, which are highly dependent on the quality of the used domain parameters. This paper proposes a novel deep learning model for HAR issues called the Long Short Term Memory Neural network with eXtreme Gradient Boosting (LSTM-XGB), developed on recurrent neural networks with eXtreme Gradient Boosting (XG-Boost). The LSTM-XGB comprises LSTM layers to understand the characteristics of the input and is capable of learning features automatically, followed by XGBoost in the final layer to forecast the class labels, which serves to enhance detection capability. When sufficient circumstances are satisfied, the LSTM-XGB model can be simpler by lowering the number of parameters. It is not essential to readjust the weighting factors throughout a backpropagation phase. Investigations using smartphone sensor data were carried out using a publicly available standardized HAR dataset called the UniMiB-SHAR dataset, which contains activities associated with everyday life and activities related to falls. The experiments show that the LSTM-XGB provides much superior identification capabilities than other standard deep learning techniques, with the highest accuracy of 92.59%. Furthermore, the LSTM-XGB models surpass the existing best-practice models on the same dataset.
Information Fusion Method for Building Grassroots Teaching Organization Based on Intelligent Optimization

At present, the existing teaching construction information fusion methods are based on the classification results of teaching resources, but ignoring the global intelligent optimization of the fusion results, which leads to the deviation of the grassroots teaching organization construction information fusion results, and the fusion index value is low. Therefore, the information fusion method of the grassroots teaching organization construction based on intelligent optimization is proposed. The basic framework of information fusion is designed, and the information fusion is divided into the data layer, characteristic layer, and decision layer. The information characteristics of the construction of grassroots teaching organizations were extracted. The particle swarm optimization algorithm is used to search the maximum value of the work entropy of teaching grassroots unit, and the threshold value of teacher construction and grassroots unit work is used to realize the information fusion of teaching grassroots unit construction. Experimental results show that the proposed method has higher error rate, stability, efficiency index, and higher information fusion value and accuracy than the traditional method.
A Fast Algorithm for Graph Learning under Attractive Gaussian Markov Random Fields

We consider the problem of graph learning under Gaussian Markov random fields, where all partial correlations are nonnegative. Such model is called attractive Gaussian Markov random fields, and has received considerable attention in recent years. The graph learning problem under this model can be formulated as the ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -norm regularized Gaussian maximum likelihood estimation of the precision matrix under sign constraints. In this paper, we propose a projected Newton-like algorithm, which is computationally efficient. By exploiting the structure of the Gaussian maximum likelihood estimation problem, the proposed algorithm significantly reduces the computational cost in computing the approximate Newton direction. Then we prove that the proposed method can recover the graph edges correctly under the irrepresentability condition. Numerical results on synthetic and financial time-series data sets demonstrate the effectiveness of the proposed method.
Image Spherizing-Based Three-Dimensional Displacement Measurement Technique for Microscope with Single Camera

The detection of three-dimensional displacement, caused by micro-nano scale deformation, is of great significance for the industrial production of glass or quartz optical fiber products. Considering the detection cost and practicability, an image spherizing-based three-dimensional displacement measurement method for a monocular microscopic imaging system (a microscope with a normal industrial camera) is proposed. Differential theory tells us that an arbitrary large-scale deformation can be approximatively considered as the sum of slope-shaped deformations. In this paper, an ordinary slope model is presented by the idea of image spherizing and differential theory, which reveals the mathematical relation between in-plane displacement and out-of-plane displacement in the deformed images captured by an optical microscope. After obtaining the in-plane displacement field, the out-of-plane displacement field can be extracted from the in-plane displacement field by the ordinary slope model, which indicates that the out-of-plane displacement information carried by the in-plane displacement is fully utilized. Simulation and application experiments are performed to demonstrate the effectiveness and all the absolute errors of the measurement by optical microscope with a magnification of 50× are less than 0.2 μm. Unlike conventional methods, the new method does not need a phase unwrapping process during detection and has many attractive characteristics such as low-cost, simple arrangements and good applicability. Because the measurement precision of this technique can meet the detection requirement, it has been applied to the industrial detection of glass or quartz optical fiber products.
Contexts and Context-awareness Revisited from an Intelligent Environments Perspective

Context is a useful concept somehow unconsciously used by humans in daily life problem-solving. Recently, several subareas of computer science have been increasingly trying to rely on this concept to design systems with practical use in certain predefined circumstances. The perception is that imbuing in the system certain context-awareness qualities can support intelligent decision-making in specific practical situations. Despite a significant number of implemented systems that aim at providing context-awareness, there is a lack of commonly accepted and used methodologies and tools. At the root of this, is the lack of agreement on a set of good principles or standards, which can act as a guide to the scientific community and the developers interested in this class of systems. There have been some extensive surveys on the use of context, still there is no theoretical corpus emerging that we can use to discuss the essential concepts making up the fabric of contexts and its use by system developers. Here we attempted such enterprise at a level, which is more formal than popular surveys, in a way that is not implementation dependent and in a way that highlights key concepts of relevance to developers. We reassessed first the basic concepts identifying the need to more prominently consider system beneficiaries’ satisfaction. We then transfer explicitly these values to a more formal outline of the basic componentgs and the operations which emerge as relevant. We identify and highlight the tasks of context activation, comparison, influence, construction, and interaction. We hint at how these may work in practice and explained these through examples. We show how the theory is flexible enough by generalizing it to multiusers so that optimization of global preferences and expectations is used to drive system development and system behavior.
Chebyshev Polynomial Broad Learning System

The broad learning system (BLS) has been attracting more and more attention due to its excellent property in the field of machine learning. A great deal of variants and hybrid structures of BLS have also been designed and developed for better performance in some specialized tasks. In this paper, the Chebyshev polynomials are introduced into the BLS to take advantage of their powerful approximation capability, where the feature windows are replaced by a set of Chebyshev polynomials. This new variant, named Chebyshev polynomial BLS (CPBLS), has a light structure with a reduction in computational complexity since the sparse autoencoder is removed. Instead, the dimension of each input sample is expended by n + 1 Chebyshev polynomials, mapping the original feature into a new feature space with higher dimension, which helps to classify the patterns in training. The proposed CPBLS is evaluated by some popular datasets from UCI and KEEL repositories, and it outperforms some representative neural networks and neuro-fuzzy models in terms of classification accuracy. The CPBLS also show some advantages over the recent developed compact fuzzy BLS (CFBLS) which indicates its great potential in future research and real-world applications.
Face Rotation and Recognition Based on Attention Mechanism and Generative Adversarial Networks

Face rotation can enrich imbalanced data and improve face recognition performance. However most of the previous methods focus on identity information. Therefore, this paper firstly proposes a novel attended pose-guided generative adversarial network (APGAN) to synthesize an arbitrary target posed photorealistic human face from the original one, while maintaining the identity and the expression information. The proposed model is mainly composed of heatmap-based generator with attention and dual discriminators with attention. Adaptive attention mechanism is proposed to concentrate on critical facial organs and maintain local structure during face rotation, for achieving more precise information preservation. Integrated expression learning exploits the shared low-level features and the inherited correlation between the two tasks, furtherly improves the expression recognition capability. Attribute purification, dynamic loss weights and alternative dataset training strengthen the performance and alienate the overfitting problem together. APGAN model is trained on small-scale dataset KDEF, challenging the robustness of the networks. The extensive experimental results on the qualitative analysis and the quantitative comparison, demonstrate that the proposed model outperformances the previous face rotation methods and expression recognition methods.
Cybersecurity Risk Assessment for Insurance in Thailand using Bayesian Network Model

This study presents hypothesis algorithm research about factors in Cyber-attacks due to lack of information security standard, Whatever the hazard, cybersecurity threats have become pervasive and continue to every Business of the digital might affect considering insurance. Bayes' theorem is a technique that classifies data type (Classification) used to this study and also a technique that uses data mining or analysis to data type and the relationship of between data and information. This technique is suitable for creating a prototype to predict the value of data or, in other terms, the occurrence of any event in the future by using samples of data that have been sorted (Predictive Modeling). Probability theory is used to estimate likelihood of uncertainty in the risk events or Cyber-attacks.
Cross-lingual timeline summarization

Timeline summarization methods analyze times-tamped, topic-specific news article collections to select the key dates representing the event flow and to extract the most relevant per-date content. Existing approaches are all tailored to a single language. Hence, they are unable to combine topic-related content available in different languages. Enriching news timelines with multilingual content is particularly useful for (i) summarizing complex events, whose main facets are covered differently by media sources from different countries, and (ii) generating news timelines in low-resource languages, for which there is a lack of news content in the target language.This paper presents three alternative approaches to address cross-lingual timeline summarization. They combine state-of-the-art extractive summarization methods with machine translation steps at different stages of the timeline generation process. The paper also proposes novel Rouge-based evaluation metrics customized for cross-lingual timeline summarization with a twofold aim: (i) quantifying the ability of the cross-lingual process to enhance available content extraction in the target language and (ii) estimating summarizer effectiveness in conveying additional content from other languages. A new multilingual timeline benchmark dataset has been generated to allow a thorough analysis of the factors that mainly influence summarization performance.
Formalizing and Verifying the Behaviors of Interacting Smart Contracts Using Model Checking

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AGRFNet: Two-stage cross-modal and multi-level attention gated recurrent fusion network for RGB-D saliency detection

RGB-D saliency detection aims to identify the most attractive objects in a pair of color and depth images. However, most existing models adopt classic U-Net framework which progressively decodes two-stream features. In this paper, we decode the cross-modal and multi-level features in a unified unit, named Attention Gated Recurrent Unit (AGRU). It can reduce the influence of low-quality depth image, and retain more semantic features in the progressive fusion process. Specifically, the features of different modalities and different levels are organized as the sequential input, recurrently fed into AGRU which consists of reset gate, update gate and memory unit to be selectively fused and adaptively memorized based on attention mechanism. Further, two-stage AGRU serves as the decoder of RGB-D salient object detection network, named AGRFNet. Due to the recurrent nature, it achieves the best performance with the little parameters. In order to further improve the performance, three auxiliary modules are designed to better fuse semantic information, refine the features of the shallow layer and enhance the local detail. Extensive experiments on seven widely used benchmark datasets demonstrate that AGRFNet performs favorably against 18 state-of-the-art RGB-D SOD approaches.
User-Centered Design to Improve Personal Development In Comic Application

Based on the results of a survey on the COMIC application, which was conducted on 33 users with an age range of 20–40 years. There is data, that the User Interface of this application is very irrelevant to the millennial generation. There are several difficulties experienced by millennial employees in using the COMIC application so that there is no match between the needs of the user and the application. The method used in this research is User-Centered Design (UCD). Through this method, the UI/UX in the COMIC application can be redesigned, by positioning millennial employees at Telkom Corporate as the center of research objects in conforming to their needs. This research has specific objectives, namely: eliciting (generating UML artifacts in the form of Requirement Statements, and making Usecase Diagrams), improvements coach booking flow through U secase Diagram modeling supported by the mockup design, and measuring user suitability through Usability Testing. The results of this study are UI development references in the form of Requirement Statements, namely Functional Requirements, improvement of the coach booking flow through Usecase diagrams which have 3 actors and 8 usecases, Re-design mockup, and usability testing measurements that produce each category, namely Effectiveness = 97%, Efficient = 93%, and Satisfaction = 84,5%. The measurement of these 3 aspects is above average and this application has achieved a conformity between the user and the application.
Implementation of Information and Communication Technologies in the Process of Future Psychologists' Training in Consideration of Their Learning Motivation

This article covers the topicality of the implementation of information and communication technologies in the process of future psychologists' professional training. We have studied students’ attitude toward the implementation of educational new forms and established the type of students' learning motivation. The results of the empirical research have indicated that students consider it appropriate to implement online forms of learning in the educational process. We have found out that part-time students receiving the second education are more focused on mastering professional knowledge and skills, and there are students who are focused only on obtaining the higher education diploma among full-time students. We have identified five groups of obstacles that arise during the implementation of online learning forms in the educational process of students-psychologists and three groups of development resources that are necessary for the effective implementation of the educational process in the online format. Thus, we see the prospects for further research in the study of the level of digital competence development among the University teachers of psychological courses in higher education and their attitude toward the implementation of information and communication technologies in the educational process of future psychologists.
Comparative analysis of solar power generation prediction system using deep learning

Due to the seriousness of environmental pollution, modern society is making efforts to switch from fossil fuel-centered energy to new and renewable energy worldwide. Among these new and renewable energies, solar energy is currently attracting attention due to its high growth potential. Among the renewable energy currently used, solar energy can generate a large amount of power without air pollutants and is widely used due to its high utilization. However, it is difficult to accurately predict the amount of power generation because solar energy, which is the source of energy from the sun, is greatly affected by seasons, weather, and installation environment. Therefore, this paper compares and analyzes the prediction and accuracy of solar power generation according to the environment through weather forecasts provided by the Meteorological Administration, using Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) learning models.
Classification Approach for Industry Standards Categorization

In the industrial sectors, it requires legal experts for analyzing and identifying complex and large industry standards promulgated by different organizations. However, the laws associated with the industry standards are varied depending on the structure of each project task. A repetition of categorizing and analyzing conducts whenever the project handles. It is a waste of resources and limited time for project establishment. Therefore, designing an independent and flexible system without relying on administrators or legal experts encourages reducing costs for operations in the long run. This study proposes a classification approach throughout the text classification method for detecting industry-standard keywords that are relevant to legal categories. As a result, it significantly improves the efficiency and accuracy of assessment results without any repetition and cooperation of specialized operators.
Enhancing Performance of IR Ship Detection with Baseline AI Models over a new Benchmark

Classical vision applications mainly extract features from IR ship images using saliency or region based segmentation algorithms. However due to the complex scenes having intensity in-homogeneity, sea clutter and noise, success of these algorithms is limited. In order to bridge this gap, in this paper we focused on automatic IR ship detection and semantic segmentation problem with the perspective of deep learning based algorithms. At first we have enhanced the previously published IRShips1 dataset to IRShips2 by increasing 18 scenes to newly labeled 178 diverse set of scenes. Then for the ship semantic segmentation task we have trained a UNet architecture with a ResNet based backbone. We have used transfer learning and data augmentation to overcome the limitations of small size of dataset. Experimental results show significant improvement in both efficiency (FPS) and accuracy (IoU and F-Score) as compared to the existing GBVS based algorithm on both IRShips1 and IRShips2 datasets. Finally we have also labeled our dataset for bounding box based detection and showed benchmark results (mAP) of two state of the art object detectors i.e.yoloV4 and yoloV5. We hope that our new IRShips2 dataset along with results of proposed baseline AI models will serve a useful benchmark for the research community.
An Efficient Algorithm to Compute the Linear Complexity of Binary Sequences

Binary sequences are algebraic structures currently used as security elements in Internet of Things devices, sensor networks, e-commerce, and cryptography. In this work, a contribution to the evaluation of such sequences is introduced. In fact, we present a novel algorithm to compute a fundamental parameter for this kind of structure: the linear complexity, which is related to the predictability (or non-predictability) of the binary sequences. Our algorithm reduced the computation of the linear complexity to just the addition modulo two (XOR logic operation) of distinct terms of the sequence. The performance of this procedure was better than that of other algorithms found in the literature. In addition, the amount of required sequence to perform this computation was more realistic than in the rest of the algorithms analysed. Tables, figures, and numerical results complete the work.
Data Mining Technology and Its Application in Multi-sensor Data Processing

Big data contains huge data value, can effectively promote the development of university information; However, there are many difficulties in the application of big data in multi-sensor data processing. In view of the practical problems faced by the current multi-sensor data processing and big data applications, the overall architecture design and the construction of core modules of multi-sensor data processing are carried out, including data acquisition, data governance, data storage, real-time computing and big data algorithm, data management, application service center and data security center. The application of multi-sensor data processing in data mining shows that it can meet the requirements of multi-sensor data processing and provide useful reference for multi-sensor data processing.
New Signature Scheme Based on Elliptic Curve and Factoring Problems Using Chaotic Map

In this paper, we present the new signature scheme based on elliptic curve (EC) and factoring (FAC) problems using chaotic maps (CMs). The newly developed scheme requires only minimal and low-complexity computations, which makes it very efficient. We compare our scheme with the other schemes with respect to signature generation cost, signature size, and verification cost and show that our scheme is superior to the other schemes. To the best of our knowledge, this is the first time a signature scheme based on EC and FAC problems using CMs has been proposed.