Reviewerly/exHarmony huggingface dataset:
Comparing the robustness of U-Net, LinkNet, and FPN towards label noise for refugee dwelling extraction from satellite imagery

Extracting footprints of refugee dwellings from satellite imagery supports dedicated humanitarian operations. Recently, deep-learning-based approaches have been proved to be effective for this task. However, such research is still limited due to the lack of cleaned labels for supervised-learning-based models. This research compares the performance of noisy labels from past humanitarian operations and cleaned labels by manual annotation in three classical deep learning architectures (U-Net, LinkNet and Feature Pyramid Network (FPN)) and twelve backbones (VGG16, VGG19, ResNet-18, ResNet-34, DenseNet-121, DenseNet-169, Inception-v3, InceptionResnet-v2, MobileNet-v1, MobileNet-v2, EfficientNet-B0, EfficientNet-B1). The results turn out that even though cleaned labels outperform noisy labels, the noisy labels have a high potential to replace cleaned labels because producing cleaned labels requires much more time, and predicted footprints of models trained with noisy labels are promising in humanitarian applications. Besides, the performance of the selected architectures and backbones is similar in general. Overall, FPN with VGG16, LinkNet with DenseNet-121, and U-Net with EfficientNet-B0 outperform other combinations. If considering both accuracy and training time, U-Net with VGG16 and LinkNet with ResNet-18 could be two alternatives for future research.
Path-finding and Planning in a 3D Environment An Analysis Using Bidirectional Versions of Dijkstra’s, Weighted A*, and Greedy Best First Search Algorithms

Due to its intricacy, path-finding is one of the most difficult optimization issues. As a result, evolutionary algorithms are favoured when it comes to finding viable solutions. However, when the number of control points and restrictions grows, computing a possible solution in a wider area consumes too much time. At this point, increasing the number of units to find a path collaterally decreases total execution time but adds to the problem’s complexity. Therefore, this problem is NP-hard, and heuristic-based algorithms have been employed to tackle it effectively. In this study, we have used bidirectional versions of Dijkstra’s, Weighted A*, and Greedy Best First Search Algorithms to find a path in a 26-neighbor 3D grid. We further discussed the efficiency obtained due to the use of two different data structures such as priority queue and priority queue based on min-heap to store the g-values.
Modernized Application Development Using Optimized Low Code Platform

Today, Low Code Development Platforms (LCDP) like Mendix, OutSystems, Zoho creators, Lansa, Quick base, and Salesforce works for modernize regular processes and quickly deliver new digitized applications. These platforms are capable to generate desktop apps, mobile apps, database apps and general-purpose apps. Basically, LCDPs has Integrated Development Environment (IDE) provides drag and drop, visual modelling, rapid application development and automatic code generation capabilities which makes them more popular. In proposed low code system, we have introduced optimization techniques for code to improve time and space complexity as well as to increase the sustainability of software. These tools often have What You See Is What You Get (WYSIWYG) editors and drag-and-drop interfaces to make development even easier. Once User Interface (UI) or visual model is prepared by using required tools/components; the respective source code will be automatically generated and optimized, then the application is ready for user to use.
A Comparative Analysis of Regression Algorithms with Genetic Algorithm In The Prediction of Breast Cancer Tumors

Over the last few decades, breast cancer has become a major health concern worldwide, particularly in the women community, as its root cause is not always known and most times it is diagnosed in advanced stages which leads to high death rate. In more recent times, machine learning techniques have been employed as computer-aided diagnosis tools for breast cancer prediction These machine learning techniques have the capacity to classify and predict this cancer into benign or malignant. The main contribution of this study is to find a model which is most suitable for predicting this kind of tumor cell. Genetic Algorithm is applied as Feature Selection method to the Wisconsin Breast Cancer dataset to select the subsets of input features that are most relevant to the target variable. We compared five machine learning regression classifiers were considered, Linear regression, Logistic regression, Random Forest, Decision Tree and Support Vector Regression. The Random Forest classifier obtained the best precision and performance accuracy. This study contributes towards the enhancement of medical technology for prediction of breast cancer, which will not only improve the well-being and health of the female community but also reduce mortality rate related to breast cancer.
Distributed Ledger Technologies for Cellular Networks and Beyond 5G: a survey

Cellular networks have played a critical role in building today's Internet.However, they are facing more and more challenges such as softwarization and programmability, decentralization, as well as opening to new business models, while keeping a very high level of trust and reliability.DLT (Distributed Ledger Technology) is a promising field to address these challenges in an innovative way.In this paper, we present a comprehensive analysis of DLT applications for cellular networks, covering the Radio Access Network (RAN), Core Network (CN), Applications & services, as well as Inter-actor communication & cooperation.
To Fork or Not To Fork? Bitcoin Forks’ Success Analysis Using Twitter Data: Preliminary Results

Bitcoin is a decentralized cryptocurrency. It is open-source; its design is public, nobody owns or controls it and everyone can take part. And Bitcoin, just like any other open-source projects, has been subject to forks.In this paper, we will consider some popular Bitcoin forks and we will examine if we can find a link between the value of the fork (i.e. its price) and the overall sentiment of the fork based on Twitter data. The forks we consider here are: Litecoin (LTC), Bitcoin Cash (BCH), Bitcoin Gold (BTG), Bitcoin Diamond (BCD), Bitcoin Private (BTCP), Bitcoin Atom (BCA), and Bitcoin SV (BSV). From the results, we can state that, for most cryptocurrencies studied here, the number of tweets follows the price trend more closely than the sentiment evolution does.The findings can have practical implications for both researchers and practitioners, indeed the results: (i) can be a stepping stone for future research on hard forks, (ii) can help practitioners identify the relevant indicators for hard forks price evolution.
Open Action Recognition by A 3D Convolutional Neural Network Combining with An Open Fuzzy Min-Max Neural Network

The 3-dimensional convolution neural network (3D CNN) has demonstrated a high prediction power for action recognition, when the inputs belong to the known classes. In a real application, however, if considering the inputs from unknown classes, previous studies have revealed that some prediction results can have high softmax scores falsely for known classes. That is called the open set recognition problem. Recently, a series of statistical methods based on an openmax approach have been proposed to solve the problem in 2D image data. However, how to apply the approach to video data is still unknown. Without using a prior statistical model, we propose a two-stage approach for open action recognition in this paper. A 3D CNN model is trained in the first stage. Then, the activation vector data, the output from the activation layer, are extracted as the feature data for training a fuzzy min-max neural network (FMMNN) as a classifier in the second stage. Since the value ranges of an activation vector are not limited between 0 and 1, an open FMMNN with a new fuzzy membership function without the normalization of input data is proposed and then constructed by the feature data. Finally, the prediction output is selected by the class with the maximum membership value. In the results, two separated datasets of mouse action videos were used for the training and the prediction test, respectively. We found that the proposed method can indeed improve the prediction performance. Moreover, using the human action and random background videos as two unknown datasets, we also demonstrated that the prediction outputs from known and unknown sets can be distinguished by a single threshold. In short, the proposed open FNNMM can not only improve the prediction performance from the inputs from known classes but also detect the inputs from unknown classes.
DePhi: Decentralizing Philanthropy via Blockchain for Traceable Micro-transactions

Today, content creators or influencers around the world are heavily reliant on digital platforms to mediate their revenue. Moreover, they use online platforms to gather funds for social causes through philanthropic campaigns. However, this raises concerns over the lack of transactional transparency of the campaigns and causes influencers to struggle attaining donors’ trust and interest in charitable causes. In this paper, we present DePhi, a blockchain-based solution to ensure transparency in philanthropy campaigns. Using a blockchain network, we record donations in the form of micro-transactions paid to content creators through smart contracts. Moreover, we develop a browser extension through which donors can easily track their donations in real-time. We use the Interledger Protocol to develop payment options in different cryptocurrencies.
BeSharing: A Copyright-aware Blockchain-enabled Knowledge Sharing Platform

In response to the coronavirus pandemic, COVID-19, the use of online media and teaching tools has soared, leading to rampant cheating and plagiarism. To provide a better online environment, A copyright-aware Blockchain-enabled Knowledge Sharing platform named as BeSharing, enables students to share part of their assignments or ideas without the worry of being plagiarized. Thanks to blockchain technology which ensures the safety of encrypted shared files and the immutability of the shared records, the intellectual property rights of students can be protected whenever plagiarism issues occur. At present, we have released the platform with the realization of relevant functions and finished the alpha test. We believe this platform has great potential to foster collaboration among students while protecting their ideas.
Deep Learning-Based Dementia Prediction Using Multimodal Data

In this paper we propose a deep architecture to predict dementia, a disease which affects around 55 million people all over the world and makes them in some cases dependent people. To this end, we have used the DementiaBank dataset, which includes audio recordings as well as their transcriptions of healthy people and people with dementia. Different models have been used and tested, including Convolutional Neural Networks for the audio classification, Transformers for the text classification and a combination of both models in a multimodal one. These models have been tested over a test set, obtaining the best results from the text modality, achieving a 90.36% of accuracy on the detection of dementia task.
Demand-Side Mobile Money Drivers of Financial Inclusion: Minimum Economic Growth Thresholds for Mobile Money Innovations

This study provides minimum economic growth (or GDP growth) critical masses or thresholds that should be exceeded in order for demand-side mobile money factors to favorably drive mobile money innovations for financial inclusion in developing countries. The considered mobile money innovations are mobile money accounts, the mobile phone used to send money, and the mobile phone used to receive money. The empirical evidence is based on tobit regressions. For positive net relationships that are established, an extended analysis is engaged to provide minimum GDP growth levels required to sustain the positive net nexuses. From this extended analysis, in order for economic growth to modulate demand-side mobile money drivers to favorably influence mobile money innovations, minimum GDP growth rates are (i) 3.875% for the nexus between bank accounts and the mobile phone used to send money; (ii) 3.769% for the relationship between automated teller machine (ATM) penetration and the mobile used to send money; and (iii) 3.666% for the nexus between ATM penetration and the mobile phone used to receive money.
IEEE VAST Challenge 2021 Winner: Visual Analytics for Spatial–Temporal Situation Awareness

IEEE VAST Challenge 2021 provides fruitful data to test the visual analytics capability of participants. We summarize our work in this article. Trajectory data and consumption data contain a lot of information, such as consumption patterns, behavior characteristics, and so on. The information can provide favorable clues for law enforcement departments to crack a case about missing employees. We designed a visual analytics system called Sundial for spatio-temporal situation awareness with multidata fusion. It contains three views, that is, consumption view, temporal behavior view, and spatial-temporal map. With the system, analysts can effectively identify the consumption and behavior patterns of employees, and detect the suspicious activities and informal or formal relationships. Through case analysis, we illustrated how to use the system and obtain effective information.
Phenotyping in clinical text with unsupervised numerical reasoning for patient stratification

Phenotypic information of patients, as expressed in clinical text, is important in many clinical applications such as identifying patients at risk of hard-to-diagnose conditions. Extracting and inferring some phenotypes from clinical text requires numerical reasoning, for example, a temperature of 102°F suggests the phenotype Fever. However, while current state-of-the-art phenotyping models using natural language processing (NLP) are in general very efficient in extracting phenotypes, they struggle to extract phenotypes that require numerical reasoning. In this article, we propose a novel unsupervised method that leverages external clinical knowledge and contextualized word embeddings by ClinicalBERT for numerical reasoning in different phenotypic contexts. Experiments show that the proposed method achieves significant improvement against unsupervised baseline methods with absolute increase in generalized Recall and F1 scores of up to 79% and 71%, respectively. Also, the proposed method outperforms supervised baseline methods with absolute increase in generalized Recall and F1 scores of up to 70% and 44%, respectively. In addition, we validate the methodology on clinical use cases where the detected phenotypes significantly contribute to patient stratification systems for a set of diseases, namely, HIV and myocardial infarction (heart attack). Moreover, we find that these phenotypes from clinical text can be used to impute the missing values in structured data, which enrich and improve data quality.
Block-Based Privacy-Preserving Healthcare Data Ranked Retrieval in Encrypted Cloud File Systems

The Internet of Medical Things (IoMT) is an important application of the Internet of Things in health care. In IoMT, efficiency and user privacy are crucial for cloud storage and retrieval of healthcare data documents. Existing schemes, however, often suffer from inefficient retrieval and increased risk of privacy disclosure when dealing with massive data. We propose here a new Efficient Encrypted Parallel Ranking (EEPR) search system, block-based and privacy-preserved, for encrypted cloud healthcare data. We design a parallel binary search tree structure in block and propose a parallel retrieval algorithm adaptable to such a structure. A quantitative analysis through the information retention index shows that our scheme demonstrates better search performance. In addition, feature vectors generated from our scheme are difficult to be reversely analyzed due to unexplainability, enhancing privacy protection for patients and researchers. A formal security analysis shows that our EEPR scheme is resistable to known background attack, and yields a lower time complexity and significantly improves search efficiency as well as accuracy over existing schemes.
A Proactive Explainable Artificial Neural Network Model for the Early Diagnosis of Thyroid Cancer

Early diagnosis of thyroid cancer can reduce mortality, and can decrease the risk of recurrence, side effects, or the need for lengthy surgery. In this study, an explainable artificial neural network (EANN) model was developed to distinguish between malignant and benign nodules and to understand the factors that are predictive of malignancy. The study was conducted using the records of 724 patients who were admitted to Shengjing Hospital of China Medical University. The dataset contained the patients’ demographic information, nodule characteristics, blood test findings, and thyroid characteristics. The performance of the model was evaluated using the metrics of accuracy, sensitivity, specificity, F1 score, and area under the curve (AUC). The SMOTEENN combined sampling method was used to correct for a significant imbalance between malignant and benign nodules in the dataset. The proposed model outperformed a baseline study, with an accuracy of 0.99 and an AUC of 0.99. The proposed EANN model can assist health care professionals by enabling them to make effective early cancer diagnoses.
Variable-to-Variable Huffman Coding: Optimal and Greedy Approaches

In this paper, we address the problem of m-gram entropy variable-to-variable coding, extending the classical Huffman algorithm to the case of coding m-element (i.e., m-grams) sequences of symbols taken from the stream of input data for m>1. We propose a procedure to enable the determination of the frequencies of the occurrence of m-grams in the input data; we formulate the optimal coding algorithm and estimate its computational complexity as O(mn2), where n is the size of the input data. Since such complexity is high in terms of practical applications, we also propose an approximate approach with linear complexity, which is based on a greedy heuristic used in solving backpack problems. In order to verify the practical effectiveness of the proposed approximate approach, experiments involving different sets of input data were conducted. The experimental study shows that the results obtained with the approximate approach were, first, close to the optimal results and, second, better than the results obtained using the popular DEFLATE and PPM algorithms in the case of data that can be characterized by highly invariable and easy to estimate statistics.
Influence of Educational Informatization Based on Machine Learning on Teaching Mode

In recent years, with the rapid progress of educational information techniques, the application of computer teaching methods in school education has also shown a constantly changing trend. How to change the traditional classroom structure, improve the teachers’ information literacy, build a new information-based teaching format, and promote the integration of information technique and pedagogy have become an important topic of the current educational research study. Machine learning is a key technology in artificial intelligence and is widely used in various fields. The core of educational informatization is to carry out personalized learning, and the main auxiliary technology of personalized learning is machine learning. So, this article studied the influence of machine learning-based educational informatization on the teaching mode. Through the investigation and research methods, this article found that the educational informatization teaching mode based on machine learning is helpful to improve students’ autonomous learning ability, comprehensive ability, and learning effect. In the information-based teaching mode, the learning effect reached a maximum of about 90, with an average increase of 12, which showed that the educational information-based teaching mode based on machine learning is feasible.
Dual-Server Identity-Based Encryption with Authorized Equality Test for IoT Data in Clouds

The massive amounts of data collected by Internet of things (IoT) devices can be stored in clouds to solve the problem of the low storage capacity of IoT terminals. However, the privacy and security of outsourced IoT data may be compromised on the cloud side. Traditional cryptographic technologies can protect data privacy but require the user to retrieve the data for decryption and further processing, which would bring vast amounts of bandwidth and computation burden to users. This paper proposes a dual-server identity-based encryption scheme supporting authorized ciphertext equality test (DS-IBE-AET), where two noncolluding servers with authorizations from users can collaboratively carry out an equality test on outsourced IoT ciphertexts without decrypting the data. DS-IBE-AET can resist offline keyword guessing attacks confronted by existing encryption schemes with equality test in the single server model. Security analysis demonstrates that the proposed DS-IBE-AET scheme offers unforgeability for private keys of users and servers and confidentiality protection for outsourced IoT data and authentication tokens. The performance analysis indicates the practicality of our DS-IBE-AET construction for securing outsourced IoT data in clouds.
Analysis on the Ecological Concept of Ternary Interactive Education in Business English Writing

In order to improve the effect of business English writing education, this paper combines intelligent algorithms to study the ecological concept of ternary interactive education in business English writing and jointly identifies interference and source signals. Moreover, this paper derives the likelihood function of the received signal under the modulation mode of the interference and the source signal and jointly considers the information of the interference. The modulation recognition can well reduce the influence of interference on algorithm performance. In addition, this paper simulates the recognition performance of the likelihood function-based algorithm when the system has frequency offset and asynchronous time offset. Finally, this paper combines experimental research to verify that the business English education system based on the ternary interaction concept proposed in this paper can effectively improve the effect of business English writing education.
Terrorism Detection and Approaches to Fairness: A Brief Literature Review

With the expansion of social media platforms (Facebook, Twitter, and other online forums), the internet has become a highway for terrorist activity. It is essential to develop models that can automate the detection of various forms of terrorist activity. However, with the increase in the use of algorithms for decision-making, there is an increase in the risk that these algorithms may promote human biases in the data sets because of social relationships captured in the data. Additionally, the decision-making algorithm themselves can be shown to be biased even with unbiased data. In this paper, we survey SOTA algorithms for terrorist activity detection. Additionally, we survey SOTA techniques for fairness bias mitigation. We end by proposing future work based on gaps found in our survey.