Reviewerly/exHarmony huggingface dataset:
Insurrectional and Pandoran Democracy, Military Perversion and The Quest for Environmental Peace: The Last Frontiers of Ecopedagogy Before Us

This chapter explores the intersection between (insurrectional and Pandoran) democracy, the postdigital context, (pervasive) militarization and ecopedagogy. Can we achieve meaningful ecopedagogy within dysfunctional forms of anti-democratic democracy? Is the appetite to build and use killing machines the consequence or the instigator of thin, docile, neutered democracy? What will it take to achieve broad-based engagement with/for the environment, which can counter and over-ride nebulous, supposedly democratic systems that are reluctant to act? While many people around the world are preoccupied with the environment, including in education, social movements, solidarity groups, animal rights groups and others, why are national and international institutions still seemingly lagging behind? These questions underpin a critical analysis of where ecopedagogy might be headed within the postdigital context.
A Targeted Privacy-Preserving Data Publishing Method Based on Bayesian Network

Privacy-preserving data publishing (PPDP) is an essential prerequisite for data-driven AI technologies, (such as data mining, machine learning, deep learning, etc.) to extract knowledge from data safely and legally. It has, as it should be, been studied and explored as a hot topic in the last decade. However, existing privacy protection mechanisms cannot take into account the following three aspects: preventing background attack, maximizing data availability, and resisting sensitive information mining. In this work, we propose a novel privacy-preserving data publishing framework, which protects privacy by releasing simulated data instead of real data. It is explored for generating data similar to the distribution of the real data by using Bayesian network. It consists of two ingredients. First, we transform the problem of data publication into the generation process of a Bayesian network, and correspondingly, the problem of privacy leakage is transformed into one kind of Bayesian inference attack. Second, we propose a re-anonymity framework, named (d, L)-injection, which flexibly resolves the impact of increased privacy protection strength on data availability. In addition, we transplant three classical privacy-preserving strategies to the generated Bayesian network, and demonstrates the effectiveness of the method through three public data sets from multiple application domains.
Object Detection in Railway Track using Deep Learning Techniques

Nowadays, deep neural networks are one of the ongoing trends which are having their uses in various kinds of fields. One of the most important applications of neural networks is the object detection framework. Object detection in railways field is the novel one, which includes the detection of obstacles on the railway tracks. But some researchers deployed this model using you only look once (YOLO) and single shot detector (SSD). We found these models to produce a lesser accuracy when compared to that of faster R-CNN. The previously mentioned models consume some time for training. To overcome the existing drawbacks, the upgraded model is being deployed by using faster R-CNN, which comprises two modules, namely regional proposal network (RPN) and fast R-CNN. It helps by detecting the obstacles such as branches, boulders, iron rods, animals, vehicles, and people. So, with the help of both the models (faster R-CNN and YOLO), we can detect the obstacles present on the track. Finally, with the quantitative and qualitative comparisons made on these two models, we chose the best fit model for this purpose. Hence, this provides the novel idea to prevent railway accidents as much as possible.
Application of Deep Learning in Detection of Covid-19 Face Mask

With the increase in the number of Covid-19 cases throughout the globe wearing face masks has proved to be effective in the prevention of the virus. In this work, we have originated a method that can detect if people are violating the rule of wearing a mask outdoors using a two-stage deep learning system. The first stage of the system detects different faces present in the input image using YOLO (You Only Look Once) model trained for the face detection and returns face ROIs. In the second stage extracted face ROI is passed through face mask detector model trained using MobileNetV2 which in turn classifies it as Mask or No mask. The dataset used for training the mask detector model is Real-World Masked Face Dataset (RMFD) and for Face Detection model is the WIDER dataset. The proposed method gives 98% accuracy for mask detection. The promising results derived from the proposed model demonstrate that the deployment of the model can be done in real-time systems.
Identify Twitter Data from Humans or Bots Using Machine Learning Algorithms with Kendalls Correlation

This study introduces bot detection techniques for identifying Twitter bot profiles and determining their susceptibility in today's online debate. Bots are common in social media. Bot accounts are problematic even though they can detect intrusions, disseminate misinformation, and disseminate unverified content, all of which can harm public opinion on a variety of issues, including product sales and election organizations. This paper proposes a framework for detecting bots using a machine learning method based on Kendall's tau-b (b) and a variety of data, including the number of followers, friends, listed counts, favorites count, and confirmed statuses counts, default profile, and default profile image.
Healing Blocks: Blockchain Enabled Decentralized Web Application for Securing Medical Records

Kumar, Shivansh Kumar, Aman Amin, Bharti RuhulCyber-attacks on hospitals in this pandemic are rising day by day and affecting numbers of organizations. Due to the weak IT infrastructure in hospitals nowadays it is very easy for hackers to breach the hospital system. The Interpol Cybercrime Threat division reported a massive surge in ransomware attacks on hospital infrastructure to make the hospitals digitally hostage. Apart from that, there are many cases in which hospitals, medical apps, private medical practitioners fragment the patient’s data which is not ideal due to corruption risks. Emerging technologies like blockchain, IPFS are some of the very promising technologies which can bring the paradigm shift in how we store our data. Decentralization is the future and can be a boon for data security in the healthcare industry. In this paper we are proposing a decentralized web application that will use IPFS for decentralized storage for medical data and blockchain will be used to secure the IPFS hashes and for access control. This application can provide the different healthcare organization a SaaS which can not only secure the patient’s data but it will also help in proper storage of data as well as management of data.
The Impact of Elon Musk Tweets on Bitcoin Price

Bitcoin is based on peer-to-peer technology where there is no need for central authority or banks as intermediaries. The network carries out Bitcoin issuing and transaction management. It is open-source, decentralized by design, nobody owns it or controls Bitcoin, and everyone can participate. Bitcoin challenges the previous payment system through its unique properties and allows compelling use cases that were not feasible in the past. Due to its decentralized properties and lack of price control, many a time, celebrities can influence its value using their social media fan followership. Twitter is becoming common place for celebrities to share their sentiments about Bitcoin. Elon Musk, a juggernaut entrepreneur of this century, is a prominent celebrity who has had a significant influence on Bitcoin and has been instrumental in promoting and criticizing Bitcoin in past months. In this paper, we analyze how Elon Musk’s tweets affect Bitcoin prices.
Feature Selection Technique-Based Approach for Suggestion Mining

The consumers share their opinions toward commercial entities like services, brands and products in discussion forums, blogs, online reviews and social media platforms. Opinion mining or sentiment analysis is one popular technique to extract positive, negative or neutral opinion of consumers on different entities. Suggestion mining is one type of opinion mining technique to extract the advices, tips or wishes that are hidden in the text. Suggestion mining is used in different applications like marketing, economics, politics and advertising. The application of suggestion mining provides the motivation for the SemEval 2019 Task 9, which introduced two subtasks such as subtask A and subtask B to differentiate the sentences into suggestions or non-suggestion sentences. Subtask A contains training and test datasets from same domain whereas subtask B contains training and test datasets from different domains. In this work, the subtask A dataset is used for classifying the given sentences into suggestion sentences or not. In this work, the experiment was conducted with different feature selection techniques such as Chi-square (CHI2), Document Frequency Difference (DFD) and Multivariate Relative Discriminative Criterion (MRDC). The feature selection techniques select the important features from the large feature set. The extracted features are used to represent the sentence vectors. These vectors are forwarded to two classification algorithms such as Support Vector Machine and Random Forest. The proposed system attained good accuracy of 83.47% for suggestion mining.
Comparative Performance Analysis of Recent Evolutionary Algorithms

Sarkar, Debojyoti Biswas, AnupamThe Evolutionary Algorithms (EAs) are employed to solve complex, multimodal, and multidimensional real-world optimization problems. The non-deterministic nature and randomness of EAs ascertain a larger diversity of the search heuristic, and hence, promises tremendous potential in reaching a better solution. Genetic Algorithm (GA) is a prime example of an EA. In the present time, with the rapid increase of EAs in numbers, a need for algorithmic evaluation has become prominent. In this paper, we have summarized the algorithmic framework of EAs and presented the computational performances of four recent EAs, tested upon a set of fifteen CEC-2015 benchmark functions.
An Improved Approach for Automated Essay Scoring with LSTM and Word Embedding

Automatic essay scoring has been shown to be an effective mechanism for quickly assessing student responses in the education system. It has already a wide variety of applications to solve, but there are evaluating the essays based on statistical features like Bag of Words (BoG), Term Frequency-Inverse Document Frequency (TF-IDF). Some of the evaluating approaches are considering the features like Word embedding with Glove, Word2Vec, One hot encoding. Both types of approaches are not fulfilling essay evaluation and not able to retrieve semantic information from essays. Here we are evaluating the essay with Word2Vec and Long Short-Term Memory (LSTM) with K-Fold cross-validation and we got an accuracy of 85.35.
Deep Learning Algorithms for Object Detection—A Study

In the field of computer vision, creating an effective object detection model is a difficult task. Object detection is the process of predicting objects and locating their instances in a given image. It is used in a wide range of applications, including self-driving cars, navigating visually impaired people in an indoor/outdoor environment, counting crowds, detecting vehicles, tracking objects, etc. Traditionally, the opencv’s feature extraction and feature detecting algorithms are employed to object detection. However, in the real world, the performance of those algorithms is unsatisfactory. In recent days, different deep learning models are available to extract and learn the objects' features to detect the objects and also the performance of these algorithms is acceptable in the real environment. This paper compares the SSD, YOLOv3 and YOLOv4 for detecting objects in the indoor environment and discusses the aspects of sparse and dense prediction for the various deep learning algorithms used for object detection.
A Novel Multiblock Region-Based Arnold Transformation for Image Watermarking Combined with DWT-PSO Technique

To ensure safe transmission of data over the internet, digital watermarking is considered to be most promising technique without compromising any duplication, tampering and copyright infringement of data. Unlike conventional Arnold scrambling method, the proposed method is bounded with two-level security in which blocks are scrambled with one key and simultaneously each block is scrambled at pixel level with second key for the same image which increases the security degree of watermark. Further, the encrypted image is used for digital watermarking which is based on DWT-PSO technique. The system also utilizes PSO algorithm to find out the optimal scaling factor to embed the watermark to obtain the highest possible robustness without failing the transparency. The proposed method's performance against imperceptibility and system robustness is evaluated using PSNR and NCC metrices. The proposed method offers a high PSNR value (54.619 dB), which is sufficient to meet any watermarking scheme's imperceptibility requirements. Furthermore, as compared to the NCC values of other international journals, the proposed method's NCC values are nearly 1 for the majority of the attacks. MATLAB tool is used to simulate the results and it generates better result with respect to imperceptibly and more robust under certain attacks as compared with existing systems.
Clustering and Classification of Low-Dimensional Data in Explicit Feature Map Domain: Intraoperative Pixel-Wise Diagnosis of Adenocarcinoma of a Colon in a Liver

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Validation for a Balancing Model Based on Ms-Bms Architecture

Battery balancing is the most important key technology of the battery management system, which affects the actual usable capacity range and performance of the entire battery module. However, it is difficult to evaluate or compare the performance of the overall BMS under the multi-series battery module structure. This study raises a balancing model, which is available for estimating the balancing performance of the BMS. A BMS based on the Master-Slave (MS) is proposed to verify the validity of the balancing model, where the balancing functions of the BMS Master and Slaves use a traditional flyback converter with MOSFET switching array and bidirectional flyback converters with photoMOS arrays, respectively; the proposed BMS takes balancing skill, balancing circuit power, equalization speed, control simplicity, modularization simplicity, and cost into account. The verification is realized with the comparisons between the balancing modelâ€™s results and the hardware experiment results under the five balancing conditions, where the average absolute error of the balancing duration results was 3.12%. The performance of the proposed BMS is also compared with the existing modular BMS architecture.
Classification of Blood Cells Using Optimized Capsule Networks

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Energy-Efficient CNN Personalized Training by Adaptive Data Reformation

To adopt deep neural networks in resource-constrained edge devices, various energy- and memory-efficient embedded accelerators have been proposed. However, most off-the-shelf networks are well trained with vast amounts of data, but unexplored users’ data or accelerator’s constraints can lead to unexpected accuracy loss. Therefore, a network adaptation suitable for each user and device is essential to make a high confidence prediction in given environment. We propose simple but efficient data reformation methods that can effectively reduce the communication cost with off-chip memory during the adaptation. Our proposal utilizes the data’s zero-centered distribution and spatial correlation to concentrate the sporadically spread bit-level zeros to the units of value. Consequently, we reduced the communication volume by up to 55.6% per task with an area overhead of 0.79% during the personalization training.
Multidimensional Financial Development, Trade Liberalization, and Productivity Growth

Only recently have researchers turned their attention to questions of financial development understood as a multidimensional concept: a combination of depth, access, efficiency, and diversity of financial providers. Using a broad-based index of financial development, we document how the different aspects of financial development affect the relationship between trade and productivity growth across countries. We find that for richer countries, financial depth and access strengthen the effects of trade openness on growth, while for poorer countries, only the degree of efficiency matters. Our results are robust to changes in sample, trade measures, and estimation strategies, and suggest the source of comparative advantage from financial development is different for countries with different levels of income.
Enhancement of Local Crowd Location and Count: Multiscale Counting Guided by Head RGB-Mask

In crowded crowd images, traditional detection models often have the problems of inaccurate multiscale target count and low recall rate.In order to solve the above two problems, this paper proposes an MLP-CNN model, which combined with FPN feature pyramid can fuse the feature map of low-resolution and high-resolution semantic information with less computation and can effectively solve the problem of inaccurate head count of multiscale people. MLP-CNN "mid-term" fusion model can effectively fuse the features of RGB head image and RGB-Mask image. With the help of head RGB-Mask annotation and adaptive Gaussian kernel regression, the enhanced density map can be generated, which can effectively solve the problem of low recall of head detection.MLP-CNN model was applied in ShanghaiTech and UCF_ CC_ 50 and UCF-QNRF. The test results show that the error of the method proposed in this paper has been significantly improved, and the recall rate can reach 79.91%.MLP-CNN model not only improves the accuracy of population counting in density map regression, but also improves the detection rate of multiscale population head targets.
Agrast-6: Abridged VGG-Based Reflected Lightweight Architecture for Binary Segmentation of Depth Images Captured by Kinect

Binary object segmentation is a sub-area of semantic segmentation that could be used for a variety of applications. Semantic segmentation models could be applied to solve binary segmentation problems by introducing only two classes, but the models to solve this problem are more complex than actually required. This leads to very long training times, since there are usually tens of millions of parameters to learn in this category of convolutional neural networks (CNNs). This article introduces a novel abridged VGG-16 and SegNet-inspired reflected architecture adapted for binary segmentation tasks. The architecture has 27 times fewer parameters than SegNet but yields 86% segmentation cross-intersection accuracy and 93% binary accuracy. The proposed architecture is evaluated on a large dataset of depth images collected using the Kinect device, achieving an accuracy of 99.25% in human body shape segmentation and 87% in gender recognition tasks.
Optimization and Combination Analysis of English Multimodule Learning Strategies based on Computational Intelligence

Due to the stagnation of foreign language teaching method research and the rapid development of cognitive psychology, in the early 1970s, the research focus of foreign language teaching changed from teacher to student research. In this context, the research of English learning strategy entered the historical stage of foreign language teaching research, and the global research of foreign language learning strategy was launched. In the past 30 years, the study of English learning strategy has been a hot research topic in the field of education in China. Finally, we conclude that English learning strategy training is the result of learning strategy training. In addition, the learning strategy training also effectively improves the English learning level of the students in the experimental class. This study aims to solve the problem of optimization and combination of English learning strategies, find effective ways and implement feasible programs, and propose theoretical and practical ways in college English teaching reforms. The connotation of “multimodule” has not been given due attention, so its role has not been fully brought into play. The following questions are answered, such as “teach students complete skills,” “train students in strategies,” “treat the teaching process correctly,” “Chinese computational intelligence has many patterns, but few practical effects” and “get rid of the strange theory that cannot be taught on the basis of computational intelligence” and “let students experience the practical process of English multimodule learning strategies and applications based on computational intelligence. The problems all seem to be related to “multimodules.”
Modeling the Potential of Cereal Crops with a Smart Village-Based GIS Approach to Support Food Security

Abstract Since 2015, government projects related to national food security have been implemented so far. This article will write a study that uses the GIS method to model the potential of food crops. The GIS web application development method uses the prototyping method with input in the form of spatial and non-spatial data which includes data on potential rice, corn, and soybeans in the Jember district, where the data was obtained from a survey at the Jember Regency Agriculture Service in the period January - August 2021 and data statistics from the book Jember in Figures 2020 and the official website of the Jember Regency BPS.
Agricultural Commodity Sales Recommendation System For Farmers Based on Geographic Information Systems and Price Forecasting Using Probabilistic Neural Network Algorithm

Abstract Since the implementation of social distancing and physical distancing due to the outbreak/pandemic of the Coronavirus (Covid-19), direct sales in the market have experienced a shortage of buyers. Farmers also share this in Indonesia, where the price game offered by collectors does not match the market price. The second problem is the mismatch of prices in each market, forcing farmers to check locations to sell their agricultural products. This problem is also experienced by the O’reng Rembangan Community Information Group (KIM), one of the community groups engaged in production to cultivate vegetable and fruit gardens in Kemuning Lor Village, Arjasa District, Jember Regency. The purpose of this research is the creation of an information system that can help farmers, especially KIM O’reng Rembangan, to obtain current market price information, receive market recommendations for agricultural products, get the nearest market from the location of farmers, and can be used by sellers to make purchases, optimize stock merchandise. This research also focuses on the prediction of agricultural commodity prices. The method used is the Probabilistic Neural Network (PNN) method to estimate the price of agricultural commodities. The resulting system in this study consists of 2 parts. The first part is the input device, which officers can use to enter the price of each agricultural commodity directly from each market. The second part is a Geographic information system used to display the forecasting results of agricultural commodity prices in each market. The forecast of agricultural commodity prices in this study has an accuracy of 98.3%.