Reviewerly/exHarmony huggingface dataset:
Disaster Preparedness Sumatra Barat Community the Relationship with Natural Intelligence, Self Efficacy and Disaster Literacy

West Sumatra is under the ring of fire and there are megathrusts, which come from two segments, namely the Siberut and Sipora-Pagai segments, earthquakes are part of people's lives, community preperedness in interacting and transacting with earthquakes is an unavoidable choice. This study seeks to examine, describe and analyze the relationship between variables of natural intelligence, self-efficacy and disaster literacy with disaster preparedness and analyze the dominant variables that influence disaster preparedness. The research method is quantitative associative correlation in analyzing the effect of one independent variable on the dependent variable. The results of the study found that first, there is a very significant positive relationship between natural intelligence and disaster preparedness; the second, there is a very significant positive relationship between self efficacy and disaster preparedness; the third, there is a very significant positive relationship between disaster literacy and disaster preparedness. From the findings of this study, it can be concluded that the community already has preparedness in dealing with earthquake disasters, but there is still a need for improvement, effective improvement by intervening on natural intelligence because this variable has proven to have a high influence on disaster preparedness.
CAPE: Context-Aware Private Embeddings for Private Language Learning

Neural language models have contributed to state-of-the-art results in a number of downstream applications including sentiment analysis, intent classification and others.However, obtaining text representations or embeddings using these models risks encoding personally identifiable information learned from language and context cues that may lead to privacy leaks.To ameliorate this issue, we propose Context-Aware Private Embeddings (CAPE), a novel approach which combines differential privacy and adversarial learning to preserve privacy during training of embeddings.Specifically, CAPE firstly applies calibrated noise through differential privacy to maintain the privacy of text representations by preserving the encoded semantic links while obscuring sensitive information.Next, CAPE employs an adversarial training regime that obscures identified private variables.Experimental results demonstrate that our proposed approach is more effective in reducing private information leakage than either single intervention, with approximately a 3% reduction in attacker performance compared to the best-performing current method.
Task-oriented attributed network embedding by multi-view features

Network embedding, also known as network representation learning, aims at defining low-dimensional, continuous vector representation of nodes to maximally preserve the network structure. Recent efforts attempt to extend network embedding to attributed networks where nodes are enriched with descriptors, to enhance interpretability. However, most of these efforts seldom consider the additional knowledge relevant to the aim of the downstream network analysis, i.e. task-related information. When they do, they are analysis-specific and thus lack adaptability to alternative tasks. In this article, a unified framework TANE is proposed to learn Task-oriented Attributed Network Embedding that jointly, maximally and consistently preserves multiple types of network information to generate rich nodes representations, robust to a variety of analyses. The framework can flexibly adapt to, and be readily modified for, different network-based tasks in an end-to-end way. The results of extensive experiments on well-known and commonly used datasets demonstrate that the proposed framework TANE can achieve superior performance over state-of-the-art methods in two commonly performed tasks: node classification and link prediction.
DI2: prior-free and multi-item discretization of biological data and its applications

Abstract Background A considerable number of data mining approaches for biomedical data analysis, including state-of-the-art associative models, require a form of data discretization. Although diverse discretization approaches have been proposed, they generally work under a strict set of statistical assumptions which are arguably insufficient to handle the diversity and heterogeneity of clinical and molecular variables within a given dataset. In addition, although an increasing number of symbolic approaches in bioinformatics are able to assign multiple items to values occurring near discretization boundaries for superior robustness, there are no reference principles on how to perform multi-item discretizations. Results In this study, an unsupervised discretization method, DI2, for variables with arbitrarily skewed distributions is proposed. Statistical tests applied to assess differences in performance confirm that DI2 generally outperforms well-established discretizations methods with statistical significance. Within classification tasks, DI2 displays either competitive or superior levels of predictive accuracy, particularly delineate for classifiers able to accommodate border values. Conclusions This work proposes a new unsupervised method for data discretization, DI2, that takes into account the underlying data regularities, the presence of outlier values disrupting expected regularities, as well as the relevance of border values. DI2 is available at https://github.com/JupitersMight/DI2
Scene Text Recognition with Cascade Attention Network

Scene text recognition (STR) has experienced increasing popularity both in academia and in industry. Regarding STR as a sequence prediction task, most state-of-the-art (SOTA) approaches employ the attention-based encoder-decoder architecture to recognize texts. However, these methods still struggle in localizing the precise alignment center associated with the current character, which is also named as the attention drift phenomenon. One major reason is that directly converting low-quality or distorted word images to sequential features may introduce confusing information and thus mislead the network. To address the problem, this paper proposes a cascade attention network. The model is composed of three novel attention modules: a vanilla attention module that attends to sequential features from the horizontal direction, a cross-network attention module to take advantage of both one-dimension contextual information and two-dimension visual distributions, and an aspects fusion attention module to fuse spatial and channel-wise information. Accordingly, the network manages to yield distinguished and refined representations correlated to the target sequence. Compared to SOTA methods, experimental results on seven benchmarks demonstrate the superiority of our framework in recognizing scene texts on various conditions.
Pengembangan Media Pembelajaran Permainan Ular Tangga Untuk Meningkatkan Motivasi Belajar Siswa dan Hasil Belajar IPS di Madrasah Ibtidaiyah

The teacher is the main axis of education to determine the progress of a country in the future in general, the teacher’s job is to provide insight or teach knowledge to students so that they have knowledge and skills in each subject area. In addition, teachers also have the responsibility of educating students to have good attitudes and behavior, whether in the school environment, at home or in the community environtment. The learning media for this game of snakes and ladders in social science learning or abbreviated as (IPS) in elementary schools / madrasah ibtidaiyah, this media is to increase student motivation and student learning outcomes. One of the problems of education is the lack of facilities and infrastructure so that a teacher is required to be as creative as possible in improving the quality of learning in class, such as developing learning media. One of them is learning media that is interesting and overcomes boredom. Of a student in learning activities, namely learning based on the game of snakes and ladders in social science learning.
An Adaptive Robust Semi-blind Watermarking in Transform Domain Using Canny Edge Detection Technique

Digital watermarking is the multimedia leading security protection as it permanently escorts the digital content. Image copyright protection is becoming more anxious as the new 5G technology emerged. Protecting images with a robust scheme without distorting them is the main trade-off in digital watermarking. In this paper, a watermarking scheme based on discrete cosine transform (DCT) and singular value decomposition (SVD) using canny edge detector technique is proposed. A binary encrypted watermark is reshaped into a vector and inserted into the edge detected vector from the diagonal matrix of the SVD of DCT DC and low-frequency coefficients. Watermark insertion is performed by using an edge-tracing mechanism. The scheme is evaluated using the Peak Signal to Noise Ratio (PSNR) and Normalized Correlation (NC). Attained results are competitive when compared to present works in the field. Results show that the PSNR values vary from 51 dB to 55 dB.
Opportunities of Blockchain Traceability Data for Environmental Impact Assessment in a Context of Sustainable Production

Supply chains face various challenges for collecting reliable, transparent, and up-to-date data due to their increased complexity and globalization. This threatens their sustainability and limits the efficiency of environmental impact assessment of products with Life Cycle Assessment (LCA) methodology. This paper explores the opportunities, limitations, and research paths for assessing products' environmental impact using blockchain-based traceability data based on a systematic literature review. Results showed that blockchains are mainly used for product traceability and could be further used for the environmental impact assessment of products. A first architecture model and integration framework was proposed in the literature for the integration of blockchain-based LCA systems. However, the maturity of blockchain and supply chain organization are the prevalent barriers to implementing these systems. Further research is essential to shape these first results with strong opportunities identified.
Age, Gender and Emotion Detection using CNN

Face is one of the most dominant features in our body. We can get many information like age, gender, etc by analyzing the face of a human. In today’s world, computer vision is been used to train machines to comprehend and understand the real world. Using several digital images fromwebcam, videos, cameras and with the help of deep learning, computers can correctly figure out and classify objects and then respond to what they “see” in real world.There are various uses of identifying age and gender from face like forensic testing, restricting access of alcohol from wending machine and adult content for young people. Emotion from a face can be used to predict human computer interaction, students/teacher’s interest in class, advertisement bots etc. Keywords: CNN; Residual neural network; RELU -Rectified Linear Unit
Aerial Dataset Integration For Vehicle Detection Based on YOLOv4

With the increasing application of UAVs in intelligent transportation systems, vehicle detection for aerial images has become an essential engineering technology and has academic research significance. In this paper, a vehicle detection method for aerial images based on the YOLOv4 deep learning algorithm is presented. At present, the most known datasets are VOC (The PASCAL Visual Object Classes Challenge), ImageNet, and COCO (Microsoft Common Objects in Context), which comply with the vehicle detection from UAV. An integrated dataset not only reflects its quantity and photo quality but also its diversity which affects the detection accuracy.The method integrates three public aerial image datasets VAID, UAVD, DOTA suitable for YOLOv4. The training model presents good test results especially for small objects, rotating objects, as well as compact and dense objects, and meets the real-time detection requirements. For future work, we will integrate one more aerial image dataset acquired by our lab to increase the number and diversity of training samples, at the same time, while meeting the real-time requirements.
Mapping environmental and climate variations by GMT: A case of Zambia, Central Africa

Zambia recently experienced several environmental threats from climate change such as droughts, temperature rise and occasional flooding and they all affect agricultural sustainability and people wellbeing through negative effects on plants and growing crops. This paper is aimed at showing variations in several climate and environmental parameters in Zambia showing spatial variability and trends in different regions of Zambia's key environmental areas (Zambezi River and tributaries), Livingstone near the Victoria Falls and central region with Muchinga Mountains. A series of 10 maps was plotted using data from TerraClimate dataset: precipitation, soil moisture, Palmer Drought Severity Index (PDSI), downward surface shortwave radiation, vapor pressure deficit and anomalies, potential and actual evapotranspiration and wind speed with relation to the topographic distribution of elevations in Zambia plotted using GEBCO/SRTM data. The data range of the PDSI according to the index values ranged from minimum at -5.7 to the maximum at 16.6 and mean at 7.169, with standard deviation at 4.278. The PDSI is effective in quantifying drought in long-term period. Because PDSI index applies temperature data and water balance model, it indicates the effect of climate warming on drought by correlation with potential evapotranspiration. The maximum values for soil moisture of Zambia show minimum at 1 mm/m, maximum at 413 mm/m, mean at 173 mm/m. This study is technically based on using the Generic Mapping Tools (GMT) as cartographic scripting toolset. The paper contributes to the environmental monitoring of Zambia by presenting a series of climate and environmental maps that are beneficial for agricultural mapping of Zambia.
Heterogeneous Multiview Crowdsensing Based on Half Quadratic Optimization for the Visual Internet of Things

For the Visual Internet of Things (VIoT), heterogeneous sensors such as visual cameras and acoustic microphones can be installed in devices to perform cognitive sensing and collect multiview data. Nonetheless, not every device has the same set of sensors due to deployment costs or sensor malfunctioning. This subsequently causes incomplete heterogeneous data, which means that data from various sensors may not be intact. For example, visual data are present, whereas audio signals are unavailable. Such phenomena may become severe when a large scale of VIoT devices are involved during crowdsensing, not to mention that missing values might occur in the collected data. In view of such, this study proposes solutions based on half quadratic (HQ) optimization to conquer the above problems in the VIoT. In this article, challenges involving incomplete heterogeneous multiview (IHM) data are investigated: IHM data reconstruction, IHM feature extraction, and IHM data recognition. Moreover, the corresponding solutions to the above challenges - HQ matrix completion, HQ supervised discrete hashing, and HQ graph neural networks - are also introduced in this article. Experiments on various HQ functions were carried out to examine their effectiveness, and the experimental results verify the proposed solutions.
Automatic Complaints Categorization Using Random Forest and Gradient Boosting

Capturing and responding to complaints from the public is an important effort to develop a good city/country. This project aims to utilize Data Mining to automatize complaints categorization. More than 35,000 complaints in Bangalore city, India, were retrieved from the “I Change My City” website (https://www.ichangemycity.com). The vector space of the complaints was created using Term Frequency–Inverse Document Frequency (TF-IDF) and the multi-class text classifications were done using Random Forest (RF) and Gradient Boosting (GB). Results showed that both RF and GB have similar performance with an accuracy of 73% on the 10-classes multi-class classification task. Result also showed that the model is highly dependent on the word usage in the complaint's description. Future research directions to increase task performance are also suggested.
A Triple Uniqueness of the Maximum Entropy Approach

Inductive logic is concerned with assigning probabilities to sentences given probabilistic constraints. The Maximum Entropy Approach to inductive logic I here consider assigns probabilities to all sentences of a first order predicate logic. This assignment is built on an application of the Maximum Entropy Principle, which requires that probabilities for uncertain inference have maximal Shannon Entropy. This paper puts forward two different modified applications of this principle to first order predicate logic and shows that the original and the two modified applications agree in many cases. A third promising modification is studied and rejected.
The Impact of ICT on Women Empowerment: Evidence from Selected South Asian Countries

During the current epoch, information and communication technology (ICT) is considered as one of the core drivers towards women empowerment. The principal concern of the underlying study is to evaluate dynamic linkage among ICT, women empowerment, fertility rate, labour force and trade openness for selected South Asian countries from 2000 to 2016 by mean-group and pooled mean-group estimators that are robust to endogeneity and heterogeneity. The outcomes infer that ICT enhances women empowerment in South Asian countries. However, fertility rate negatively contributes to women empowerment. Sensitivity analysis using fully modified ordinary least square (FMOLS) method is applied to check the validation, consistency, and robustness of the study for future policy implications. The analytics surmise future strategy for women empowerment with confined ICT expansion and discouraging fertility in South Asian countries.
Data Sharing Mechanism of Sensors and Actuators of Industrial IoT Based on Blockchain-Assisted Identity-Based Cryptography

The Industrial IoT is one of the key technologies to improve industrial production efficiency. The entire production process usually involves multiple production regions and numerous smart devices (sensors and actuators). The efficiency of the Industrial IoT is limited by this strong coupling relationship between the subsystem and the sensors and actuators. In this paper, to unleash the potential of Industrial IoT, a safe and reliable data sharing mechanism of sensors and actuators is proposed. We deployed distributed identity authentication and data proxy services in various regions. In the device authentication process, we used identity-based encryption algorithms, and we solved the trust problem between different regions by introducing a private blockchain. In addition, we designed the model of device capability (MDC) to describe the device, enabling it to be shared with a standard interface. Finally, we conducted many performance tests on the proposed mechanism. The test results verified the effectiveness and efficiency of the proposed mechanism.
Project Ariel: An Open Source Augmented Reality Headset for Industrial Applications

Some of the biggest challenges in applying Augmented Reality (AR) technologies to the industry floor are in the form factor, and safety requirements of the head worn display. This includes alleviating issues such as peripheral view occlusion, and adaptation to personal protective equipment. In this work we present the design of Project Ariel, an Open Source 3D printable display specifically designed for use in industrial environments. It is our hope that with this technology, the average tradesman can utilize the powerful visualizations AR has to offer, significantly improving their daily work flow.
Sub-Frame Evaluation of Frame Synchronization for Camera Network Using Linearly Oscillating Light Spot

Precisely evaluating the frame synchronization of the camera network is often required for accurate data fusion from multiple visual information. This paper presents a novel method to estimate the synchronization accuracy by using inherent visual information of linearly oscillating light spot captured in the camera images instead of using luminescence information or depending on external measurement instrument. The suggested method is compared to the conventional evaluation method to prove the feasibility. Our experiment result implies that the estimation accuracy of the frame synchronization can be achieved in sub-millisecond order.
Suitability Analysis of Solar Tracking PV System in the Airport Based on Glare Assessment

Due to the concerns with harmful emissions, airports have a great interest in solar photovoltaic technology. On the one hand, the vacant land areas in the airport can house solar PV arrays. On the other hand, there is an aviation safety issue due to the reflections from the solar PV arrays. This paper aims to perform a glare analysis of a conceptual solar PV array for three different solar tracking techniques. This suitability analysis is carried out for fixed-tilt, single-axis and dual-axis tracking techniques using ForgeSolar software. It is observed that a single-axis tracking solar PV system is a suitable tracking technique for the selected site. This can be attributed to zero minutes of glare duration and the highest value of energy generation. In addition to compliance with the FAA’s solar glare policy, the single-axis tracking solar PV system will generate 40 % more electricity than a fixed-tilt solar PV system. Unlike previously reported studies, the results of this study strengthen the theoretical support for tracking solar PV systems in airport locations. The findings provided in this study will be beneficial to energy professionals and serve as reference material for tracking solar PV in airports.
Introduction to Robust Statistics

Chapter 11 introduces robust statistics. It presents an approach to determine whether or not a sample follows a normal distribution. Chapter 11 continues defining robust approaches for the estimation of the location and the scale of a sample. It concludes by discussing the role of robust statistics in geochemistry.
Towards Compact Broad Learning System by Combined Sparse Regularization

Broad Learning System (BLS) has been proven to be one of the most important techniques for classification and regression in machine learning and data mining. BLS directly collects all the features from feature and enhancement nodes as input of the output layer, which neglects vast amounts of redundant information. It usually leads to be inefficient and overfitting. To resolve this issue, we propose sparse regularization-based compact broad learning system (CBLS) framework, which can simultaneously remove redundant nodes and weights. To be more specific, we use group sparse regularization based on [Formula: see text] norm to promote the competition between different nodes and then remove redundant nodes, and a class of nonconvex sparsity regularization to promote the competition between different weights and then remove redundant weights. To optimize the resulting problem of the proposed CBLS, we exploit an efficient alternative optimization algorithm based on proximal gradient method together with computational complexity. Finally, extensive experiments on the classification task are conducted on public benchmark datasets to verify the effectiveness and superiority of the proposed CBLS.