Reviewerly/exHarmony huggingface dataset:
Deep Pre-trained Contrastive Self-Supervised Learning: A Cyberbullying Detection Approach with Augmented Datasets

Cyberbullying is a widespread problem that has only increased in recent years due to the massive dependence on social media. Although, there are many approaches for detecting cyberbullying they still need to be improved upon for more accurate detection. We need new approaches that understand the context of the words used in cyberbullying by generating different representations of each word. In addition. there is a large amount of unlabelled data on the Internet that needs to be labelled for a more accurate detection process. Even though multiple methods for annotating datasets exists, the most widely used are still manual approaches, either using experts or crowdsourcing. However, The time needed and high cost of labor for manually annotation approaches result in a lack of annotated social network datasets for training a robust cyberbullying detector. Automated approaches can be relied upon in labelling data, such as using the Self-Supervised Learning (SSL) model. In this paper, we proposed two main parts. The first part is proposing a model of parallel BERT + Bi-LSTM used for detecting cyberbullying terms. The second part is utilizing Contrastive Self-Supervised Learning (a form of SSL) to augment the training set from unlabeled data using a small portion of another manually annotated dataset. Our proposed model that used deep pre-trained contrastive self-supervised learning for detecting cyberbullying using augmented datasets achieved a performance of (0.9311) using macro average F1 score. This result shows our model outperformed the baseline models - the top three teams in the competition SemEval-2020 Task 12.
On the Maximum Size of a Prefix Code

A prefix code minimal with respect to a bitstring <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula> is a prefix code where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula> is a concatenation of its codewords and it is minimal with respect to this property. What is the maximum size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M(n)$ </tex-math></inline-formula> among all minimal codes over all bitstrings of length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n?$ </tex-math></inline-formula> In this paper we determine the value of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M(n)$ </tex-math></inline-formula> for all natural numbers <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> , discuss its computational complexity, relation to the Lambert function, provide tight upper bounds, and describe how the value of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M(n)$ </tex-math></inline-formula> enables one to construct efficiently a Huffman code in the case of uniform probability distribution of the codewords.
DID We Miss Anything?: Towards Privacy-Preserving Decentralized ID Architecture

Decentralized Identity (DID) is emerging as a new digital identity management scheme that promises users complete control of their personal data and identification without central authority involvement. The World Wide Web Consortium (W3C) has drafted the DID standard and provided reference implementations. We conduct a security analysis of the W3C DID standard and the reference universal resolver implementation, focusing on user privacy in the DID resolving process. The universal resolver is the key component in the architecture that processes DID requests and DID document retrievals. Our analysis demonstrates that privacy issues can arise due to the imprudent design of the universal resolver. Furthermore, we found that side-channels in the DID document caching schemes of real-world DID services can entail privacy concerns. Motivated by our security analysis, we present a novel DID resolving design, called Oblivira, to enable obliviously DID resolving. Oblivira is a secure resolving agent with a small footprint that enforces the universal resolver to resolve requests without knowing their content. We also propose a privacy-preserving DID document caching scheme that eliminates side-channels. Our evaluation results show that Oblivira only incurs approximately 2.6% of overhead on average with different resolver settings (3, 6, and 12 threads).
Transfer and Triangulation Pivot Translation Approaches for Burmese Dialects

Parallel corpora for the languages of Myanmar (Beik, Burmese, Rakhine) are extremely scarce but a necessary requirement for machine translation R&D. Previous studies have proved that pivoting leads to better translation quality if the bridge language is closely related to the source and target language pair. The baseline study is conducted based on the three major approaches of machine translation; Weighted Finite State Transducer (WFST), Phrase-Based Statistical Machine Translation (PBSMT) and Deep Recurrent Neural Network (Deep-RNN). Based on the baseline results, this paper mainly investigated the pivot language technique for PBSMT with Burmese dialects. We employed two different pivot translation methods: transfer (sentence level) and triangulation (phrase level). We present the experimental results on Dawei-Beik, Beik-Dawei translations and Beik-Rakhine, Rakhine-Beik translation via Burmese. Both the transfer and triangulation approaches outperformed the baseline (direct translation), specifically for the Rakhine-Beik language pair. Moreover, the results of the average BiLingual Evaluation Understudy (BLEU), Character n-gram F-score (chrF), and Word Error Rate (WER) scores of the 10-fold cross-validation experiments proved that the triangulation pivot has significantly better acceleration than the transfer pivot. We plan to release the parallel corpora of Burmese dialects and present several avenues for further research.
Security Implications of IoT Applications with Cryptography and Blockchain Technology in Healthcare Digital Twin Design

Over the last few years, the world has witnessed a fast-paced digital transformation in many aspects of human life in healthcare owing to the coronavirus (COVID-19) pandemic. Business and service providers had to adapt to digital changes quickly to overcome containment challenges and survive in an ever-changing world. Healthcare-related data collection, preservation, and analysis using digital technologies are helping pandemic mitigation strategies. With the rapid development of virtual systems integration methods and data acquisition techniques, digital twin (DT) technology is ushering in a new dawn for modern healthcare services and information systems. However, IoT-based information systems are vulnerable to privacy and security-related issues. This chapter presents an information system framework that consists of IoT with blockchain technology to mitigate vulnerability issues using lightweight cryptography.
Enhanced multiobjective optimizer for GIS-based siting of solar PV plants in Red Sea Governorate, Egypt

Choosing a suitable location for solar photovoltaic (PV) plants depends on several conflicted selection criteria such as technical, economical, and social restrictions. For handling such a problem, this paper proposes two Computational Intelligence (CI) based frameworks called Genetic Algorithm with Repairing operator (GAR) and Map-Reduce-based Genetic Algorithm with a Repair operator (MRGAR). The governorate of the Red Sea in Egypt is selected as a case study which is a privileged area for harvesting solar energy. Firstly, all gathered maps and geographic information resources are manipulated. Secondly, the regarded problem is formulated as a binary-constrained multiobjective optimization problem. Finally, this problem is solved with two proposed frameworks and the results are simulated. The experimental results conclude that both frameworks are efficient for solving the regarded site selection problem. However, GAR and MRGAR have diversified performances. In particular, the percentage of gathered solar energy of GAR is better than MRGAR whereas MRGAR is significantly faster than GAR. Therefore, MRGAR is recommended to deal with large-scale problems.
An efficient load balancing technique using CAViaR-HHO enabled VM migration and replica management in cloud computing

Cloud computing is immense technology that offers distributed resources to a number of users who are present throughout the world. Cloud model is comprised of numerous virtual machines (VMs) and physical machines (PMs) to carry out user tasks effectively in a parallel manner but in some cases, the demand of the users may be high that resulting in the overloading of PMs and this condition deteriorates the performance of cloud network. For achieving effective virtualization in the cloud paradigm, energy and resource utilization are major properties that should be handled effectively and such properties are accomplished through effective management of workload by distributing load equivalently among VMs. By doing so, resource utilization of the network is enhanced and it only requires minimum energy to process the tasks. Numerous load-balancing algorithms have been introduced earlier to maintain load in a cloud environment, nevertheless, they are devoid of mitigating the number of task migrations. Hence, this research proposes an effective load balancing algorithm and replica management method using the proposed Conditional Autoregressive Value at risk by Regression Quantiles-Horse Herd Optimization (CAViaR-HHO) model. Here, the load is computed by considering some factors like Central Processing Unit (CPU), Million Instructions per Second (MIPS), bandwidth, memory, and frequency. VM migration and replica migration is effectively carried out using the proposed CAViaR-HHO model. Meanwhile, the developed method is devised by integration of Conditional Autoregressive Value at risk by Regression Quantiles (CAViaR) with Horse Herd Optimization Algorithm (HOA). However, the proposed CAViaR-HHO has achieved a load with a minimum value of 0.109, capacity with a maximum value of 0.591, resource utilization with a maximum value of 0.467, and minimum cost of 0.344. Using setup-1, when the number of tasks is 500, the capacity of the proposed method is 5.58%, 3.89%, 2.87%, 1.52%, and 0.67% higher when compared to the existing approaches namely, C-FDLA, K-means clustering + LB, Adaptive starvation threshold, EIMORM, and Dynamic replica creation method.
Helio: A framework for implementing the life cycle of knowledge graphs

Building and publishing knowledge graphs (KG) as Linked Data, either on the Web or in private companies, has become a relevant and crucial process in many domains. This process requires that users perform a wide number of tasks conforming to the life cycle of a KG, and these tasks usually involve different unrelated research topics, such as RDF materialisation or link discovery. There is already a large corpus of tools and methods designed to perform these tasks; however, the lack of one tool that gathers them all leads practitioners to develop ad-hoc pipelines that are not generic and, thus, non-reusable. As a result, building and publishing a KG is becoming a complex and resource-consuming process. In this paper, a generic framework called Helio is presented. The framework aims to cover a set of requirements elicited from the KG life cycle and provide a tool capable of performing the different tasks required to build and publish KGs. As a result, Helio aims at providing users with the means for reducing the effort required to perform this process and, also, Helio aims to prevent the development of ad-hoc pipelines. Furthermore, the Helio framework has been applied in many different contexts, from European projects to research work.
Cross-collection latent Beta-Liouville allocation model training with privacy protection and applications

Cross-collection topic models extend previous single-collection topic models, such as Latent Dirichlet Allocation (LDA), to multiple collections. The purpose of cross-collection topic modeling is to model document-topic representations and reveal similarities between each topic and differences among groups. However, the restriction of Dirichlet prior and the significant privacy risk have hampered those models' performance and utility. Training those cross-collection topic models may, in particular, leak sensitive information from the training dataset. To address the two issues mentioned above, we propose a novel model, cross-collection latent Beta-Liouville allocation (ccLBLA), which operates a more powerful prior, Beta-Liouville distribution with a more general covariance structure that enhances topic correlation analysis. To provide privacy protection for the ccLBLA model, we leverage the inherent differential privacy guarantee of the Collapsed Gibbs Sampling (CGS) inference scheme and then propose a hybrid privacy protection algorithm for the ccLBLA model (HPP-ccLBLA) that prevents inferring data from intermediate statistics during the CGS training process without sacrificing its utility. More crucially, our technique is the first attempt to use the cross-collection topic model in image classification applications and investigate the cross-collection topic model's capabilities beyond text analysis. The experimental results for comparative text mining and image classification will show the merits of our proposed approach.
Hierarchical mission replanning for multiple UAV formations performing tasks in dynamic situation

UAV should be capable of responding to the dynamic changes, timely and accurately. The mission replanning problem for multiple UAV formations is studied in this paper, and a hierarchical method is proposed to have a comprehensive consideration of the dynamic changes caused by the task and environment. First, three types of task change are analyzed, and the task reassignment approach is designed to determine the opportunity of conducting path replanning, which can make the best of the predetermined path and save more computational time. Moreover, a hierarchical strategy is presented to determine the most suitable UAV formation to perform a new task. Then a two-step path replanning approach combining the efficient rapid-exploration random tree (ERRT) algorithm and the modified consensus (MC) algorithm is developed. The waypoints are determined by the ERRT algorithm, and then the MC algorithm is applied to make the UAV formation head for the waypoints one after another. The MC algorithm also can be used in the UAV formation's dismission and a single UAV's landing. Simulation results demonstrate that the proposed hierarchical mission replanning method performs well under complicated dynamic changes. The task list of UAV formation can be updated timely and reasonably, and feasible paths can be generated rapidly by the ERRT algorithm considering different pairs of start point and destination.
Cloud probability: A new uncertain model with fuzziness and randomness properties

In order to overcome the weakness of subjectivity of variable and subjectivity of membership function in fuzzy probability the cloud probability model and its algorithm are proposed. Firstly, the representation model of cloud probability is given based on the fusion of cloud model and fuzzy probability. Then the cloud probability algorithm of continuous random variable based on slice method is proposed. Then the relationship between slice number and cloud probability is discussed. And the cloud probability algorithm of discrete random variable is given. Finally, through the application case of e-commerce intelligent decision-making based on cloud probability the effectiveness of the proposed cloud probability algorithm is verified. The research in this paper has good reference significance for dealing with the events represented by uncertain variables.
A remote sensing encrypted data search method based on a novel double-chain

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Improved breast lesion detection in mammogram images using a deep neural network

This study aimed to investigate the effect of using a deep neural network (DNN) in breast cancer (BC) detection.In this retrospective study, a DNN-based model was constructed from a total of 880 mammograms that 220 patients underwent between April and June 2020. The mammograms were reviewed by two senior and two junior radiologists with and without the aid of the DNN model. The performance of the network was assessed by comparing the area under the curve (AUC) and receiver operating characteristic curves for the detection of four features of malignancy (masses, calcifications, asymmetries, and architectural distortions), with and without the aid of the DNN model and by the senior and junior radiologists. Additionally, the effect of utilizing the DNN on diagnosis time for both the senior and junior radiologists was evaluated.The AUCs of the model for the detection of mass and calcification were 0.877 and 0.937, respectively. In the senior radiologist group, the AUC values for evaluation of mass, calcification, and asymmetric compaction were significantly higher with the DNN model than those obtained without the model. Similar effects were observed in the junior radiologist group, but the increase in the AUC values was even more dramatic. The median mammogram assessment time of the junior and senior radiologists was 572 (357-951) s, and 273.5 (129-469) s, respectively, with the DNN model, and the corresponding assessment time without the model, was 739 (445-1003) s and 321 (195-491) s, respectively.The DNN model exhibited high accuracy in detecting the four named features of BC and effectively shortened the review time by both senior and junior radiologists.
Exploration of the Dynamic Evolution of Online Public Opinion towards Waste Classification in Shanghai

Shanghai is one of the fastest-growing metropolises and the first city in China to implement mandatory waste classification. Waste classification policy of Shanghai has attracted widespread attention since its implementation in July 2019. However, previous papers have not focused on online public attitudes surrounding the implementation of a waste classification policy in Shanghai. In order to fill this gap, this paper explored the dynamic evolution of online public attitudes towards waste classification in Shanghai by using sentiment analysis technology and topic modeling technology. It was found that the proportion of negative posts each month was about 20%; therefore, online public sentiment towards waste classification in Shanghai was generally positive. Compared with the first three months of policy implementation, the public sentiment towards Shanghai's waste classification became more positive, with the exception of two special periods. Negative posts in July 2019 mainly discussed waste's environmental hazards and policy provisions. New topics in negative posts in later months focused on some specific problems, including the process of throwing away wet waste, the allocated throwing times, the number of waste cans, takeaway meal disposal, and gathering activities. Improving the factors causing the negative sentiments in the posts will help the government better implement the policy. The paper will help the government to receive higher public support for the waste classification policy in Shanghai. The present findings also have great reference significance for other cities.
KreolStem: A hybrid language-dependent stemmer for Kreol Morisien

Stemming is a technique used to transform words to their root forms. It is used in various Natural Language Processing applications to improve performance and accuracy. In this paper, the development and evaluation of the first stemmer for Kreol Morisien (KM), the native language of Mauritius, is described. This proposed stemmer is expected to unlock countless opportunities for more efficient applications in KM. The hybrid approach, which combines both rule-based and table-lookup techniques, is presented. The results of the three-fold evaluation, consisting of direct evaluation, gold standard assessment and indirect evaluation are also presented.
A Multidimensional Model for Recommendation Systems Based on Classification and Entropy

The proliferation of false and redundant information on e-commerce platforms as well as the prevalence of ineffective recommendations and other untrustworthy behaviors has seriously impeded the healthy development of these platforms. To address these issues and enhance prediction accuracy and user trust, contemporary recommendation systems often utilize additional information (i.e., side information). In this work, we propose a model to improve the recommendation quality by employing the information entropy of user-item ratings. The entropy was used as side information to reflect the global rating behavior of the user and item. We also utilized the classification of the user and item as heuristic information to improve the prediction quality. In our best result, we achieved a significant improvement of 8.2% in prediction accuracy. The model classified the items by the users’ actual preference, which is more trustworthy for users. We evaluated our model with three real-world datasets. The performance of our proposed model was significantly better than the other baseline methods. The similarity calculation method employed in the present model has the potential to mitigate the data sparsity problem associated with correlation-based similarity. The proposed weight matrix has zero sparsity. Furthermore, the proposed model has a more favorable computational complexity for prediction compared to the conventional k-nearest neighbor method.
A study of nonlinear multiview varieties

We study the nonlinear generalization of the classical multiview variety, which is a fundamental concept in computer vision. In this paper, we take the first comprehensive step to develop the nonlinear analogue of multiview varieties. To this end, we introduce a multigraded version of the saturated special fiber ring. By applying this tool, we are able to compute the multidegrees of several families of nonlinear multiview varieties.
Can Hierarchical Transformers Learn Facial Geometry?

Human faces are a core part of our identity and expression, and thus, understanding facial geometry is key to capturing this information. Automated systems that seek to make use of this information must have a way of modeling facial features in a way that makes them accessible. Hierarchical, multi-level architectures have the capability of capturing the different resolutions of representation involved. In this work, we propose using a hierarchical transformer architecture as a means of capturing a robust representation of facial geometry. We further demonstrate the versatility of our approach by using this transformer as a backbone to support three facial representation problems: face anti-spoofing, facial expression representation, and deepfake detection. The combination of effective fine-grained details alongside global attention representations makes this architecture an excellent candidate for these facial representation problems. We conduct numerous experiments first showcasing the ability of our approach to address common issues in facial modeling (pose, occlusions, and background variation) and capture facial symmetry, then demonstrating its effectiveness on three supplemental tasks.