Reviewerly/exHarmony huggingface dataset:
Factorization‐based active contour segmentation and pelican optimization‐based modified bidirectional long short‐term memory for ovarian tumor detection

Abstract This paper proposes a novel three‐dimensional convolution neural network‐based modified bidirectional long short‐term memory with pelican optimization (3D CNN based MBiLSTM with PO) algorithm for multiclass ovarian tumor detection. Initially, the International Collaboration on Cancer Reporting endometrial cancer dataset images are provided in pre‐processing phase, which uses a pre‐emphasis filter to process the input image. In the segmentation phase, pre‐processed data is then partitioned into diverse subgroups (i.e., pixels), which minimizes the complexity of images. In this paper, a factorization‐based active contour technique is employed in the effective segmentation of images. The segmented features are then extracted and classified using the 3D CNN‐MBiLSTM with PO algorithm. Finally, the experimental results are conducted and compared with various other approaches for various performance metrics. Each metric is evaluated with respect to the different number of iterations. The accuracy, sensitivity, and specificity have reached a higher value of 98.5%, 96%, and 98.25%, respectively.
Decentralized Online Learning: Take Benefits from Others’ Data without Sharing Your Own to Track Global Trend

Decentralized online learning (online learning in decentralized networks) has been attracting more and more attention, since it is believed that decentralized online learning can help data providers cooperatively better solve their online problems without sharing their private data to a third party or other providers. Typically, the cooperation is achieved by letting the data providers exchange their models between neighbors, e.g., recommendation model. However, the best regret bound for a decentralized online learning algorithm is 𝒪( n √ T ), where n is the number of nodes (or users) and T is the number of iterations. This is clearly insignificant, since this bound can be achieved without any communication in the networks. This reminds us to ask a fundamental question: Can people really get benefit from the decentralized online learning by exchanging information? In this article, we studied when and why the communication can help the decentralized online learning to reduce the regret. Specifically, each loss function is characterized by two components: the adversarial component and the stochastic component. Under this characterization, we show that decentralized online gradient enjoys a regret bound \( {\mathcal {O}(\sqrt {n^2TG^2 + n T \sigma ^2})} \) , where G measures the magnitude of the adversarial component in the private data (or equivalently the local loss function) and σ measures the randomness within the private data. This regret suggests that people can get benefits from the randomness in the private data by exchanging private information. Another important contribution of this article is to consider the dynamic regret—a more practical regret to track users’ interest dynamics. Empirical studies are also conducted to validate our analysis.
Hate-Speech Detection in News Articles: In the Context of West Bengal Assembly Election 2021

A hate speech represents an expression or phrase of offensive language. The intention behind the usage of hate speech is to abuse, dehumanize, disparage, or harass a person or a group of persons based on their race, color, gender, religion, caste, ethnicity, disability, language, belief, nationality, or other factors. Hate speech is also used to express violence, harm, or hatred against the targeted people. Nowadays, the amount of hate speech is overgrowing in social media, online newspapers, etc. It becomes very difficult to moderate the data containing hate speeches manually, as it is a tedious and time-consuming task. Therefore, an automated hate-speech detection technique is very essential. Numerous works have been done to develop the technique or tool for automatic detection of hate speeches in Twitter, Facebook, and other social media data. This paper studies hate speeches on political news articles in the context of the West Bengal Legislative Assembly Election 2021. In the computational aspect, this task is carried out in three phases. First, a political news corpus has been created, and next from that corpus a key word/phrase-based hate-speech identifier is developed. A semi-automated approach has been used to find out the set of hate-speech-related key words/phrases. Finally, the system’s performance is evaluated, and the results are investigated.
Performance Comparison of Classification Models for Identification of Breast Lesions in Ultrasound Images

Globally, breast cancer is the most common disease among women. A region endures from damage through any disease then the region is known as lesion. It is important to differentiate different types of breast lesions for proper treatment. Therefore, there is a significant impetus for the researchers in the development of computer-aided diagnostic (CAD) system that can assist clinicians in breast lesion diagnosis. This work presents the performance comparison of different renowned classifiers in terms of accuracy with which they identify the type of breast lesion in ultrasound (US) images of the given dataset. The CAD system is developed to assist the clinicians as a second opinion tool in identifying the type of breast lesion. In this system, ultrasound images are taken as inputs and the region of interest (ROI) for each US image is marked according to the shape of abnormality. Different texture features are extracted from the US images and further these images are classified into binary classes of malignant and benign using different classifiers. The performance of these classifiers is compared and it is observed that the law’s mask texture features of dimension 5 provided a maximum classification of 97.4% than other feature extraction methods applicable for the classification of two-class breast lesions.
Evaluating Quality of Machine Translation System for Digaru-English with Automatic Metrics

The machine translation output requires evaluation to measure its relevancy to the reference for checking its quality. The evaluation of the output translations can be done manually by human translator, but it requires immense amount of time and labor cost. Therefore, the automatic evaluation metrics has been introduced which is widely used for machine translation evaluation task. In this paper, the translation quality of Digaru-English translation output of 10,500 sentence pair is measured using the automatic evaluation metrics. Further, the translation output of Digaru-English from the Moses SMT system has been evaluated by the BLEU, NIST, METEOR, and TER metric. Further, manual evaluation of Digaru-English has been examined and judged based on fluency and adequacy.
Hybrid Approach for Fake Profile Identification on Social Media

Millions of people use social media platforms like Twitter, Facebook, and Instagram all around the world. People are drawn to these social media sites, and as the prevalence of social media grow, so do the security and privacy concerns that come with it. Nowadays, it is critical to ensure that we are following the correct social media account or purchasing a product from the actual consumer, as malicious users can be extremely harmful. This paper proposes a hybrid method for detecting fake social media user accounts. For detecting fake accounts, it makes use of the Instagram social media platform’s dataset. There are two steps to the hybrid approach. The first stage is to use Principal Component Analysis (PCA) which turn original variables into new uncorrelated variables, and the second stage is to use various classification algorithms, in the second stage five algorithms are used to obtain accurate results. Fake profiles are detected using naive Bayes, artificial neural networks (ANN), support vector machine (SVM), logistic regression, and K-nearest neighbors (KNN) algorithms. When the classification performances of these approaches are compared, the artificial neural network outperforms the others.
A Security Provocation in Cloud-Based Computing

Cloud computing makes a massive trend for research organizations because highly demand. There is different type of causes that many organizations depend on it and take advantage from cloud computing service. Cloud computing appears a model that requires all the primary elements of evaluation such as end-user contraption, transmission web channel, access conduction, and cloud architecture. Moreover, with the exposure of new appearance in the computing world like the 5G Internet, Internet of an object (IoB), and elegant town, the bit part of cloud evaluation will be added essential for operation and storage more data than always preparatory to. The principal advantage of cloud computing is ambidexterity, domain agreement, and superior service for the end user. So, the application of cloud evaluation in a non-identical realm is the principal stability with the ease of cloud-based resource collaboration. These incorporate many IT infrastructure, remote access servers from anywhere, we can use it through the Internet. The related safety and protection challenges in the cloud needed further investigation. Scientists from the different domains provided a possible blend to these provocations in the previously published learning area. As the expanding request of cloud computing makes a huger range toward end-user data safety. In this paper, we observe meta-inspection has been given toward cloud computing certainty. It also incorporates complete analysis based on the outcome achieved in the same. The paper gives us a logical and experiential perspective of cloud computing safety.
Recognize Meaningful Words and Idioms from the Images Based on OCR Tesseract Engine and NLTK

OCR means optical character recognition, which is a text extraction technology that works with photos, scanned data, and PDF documents. By extracting text data, OCR systems typically convert non-editable, non-searchable documents into editable, searchable files. As a result, information finding and identification from digitized files is simplified. R bindings are provided by the Tesseract package. Tesseract is a strong optical character recognition (OCR) engine with over 100 languages supported. The engine is highly customizable, allowing you to fine-tune the detection algorithms to achieve the best possible results. With the help of Tesseract OCR technology, a method for extracting texts from photos was created. Any image can be used as input for the proposed OCR system, which converts it into a searchable text document. Furthermore, this system can search for words within the generated text and display the Bengali meaning terms. It finds the words and lines first, then identifies the words, then the static character classifier classifies the character, then does analysis, and finally an adaptive classifier. It is a framework which also includes a natural language processing approach for classifying commonly used terms with Bangla meanings from the output text, in addition to OCR.
Context for Reproducibility and Replicability in Geospatial Unmanned Aircraft Systems

Multiple scientific disciplines face a so-called crisis of reproducibility and replicability (R&amp;R) in which the validity of methodologies is questioned due to an inability to confirm experimental results. Trust in information technology (IT)-intensive workflows within geographic information science (GIScience), remote sensing, and photogrammetry depends on solutions to R&amp;R challenges affecting multiple computationally driven disciplines. To date, there have only been very limited efforts to overcome R&amp;R-related issues in remote sensing workflows in general, let alone those tied to unmanned aircraft systems (UAS) as a disruptive technology. This review identifies key barriers to, and suggests best practices for, R&amp;R in geospatial UAS workflows as well as broader remote sensing applications. We examine both the relevance of R&amp;R as well as existing support for R&amp;R in remote sensing and photogrammetry assisted UAS workflows. Key barriers include: (1) awareness of time and resource requirements, (2) accessibility of provenance, metadata, and version control, (3) conceptualization of geographic problems, and (4) geographic variability between study areas. R&amp;R in geospatial UAS applications can be facilitated through augmented access to provenance information for authorized stakeholders, and the establishment of R&amp;R as an important aspect of UAS and related research design. Where ethically possible, future work should exemplify best practices for R&amp;R research by publishing access to open data sets and workflows. Future work should also explore new avenues for access to source data, metadata, provenance, and methods to adapt principles of R&amp;R according to geographic variability and stakeholder requirements.
Blockchain-Based Smart Contract for P2P Energy Trading in a Microgrid Environment

Abstract The purpose of this study is to propose a method of peer-to-peer (P2P) energy trading that allow prosumers with energy deficiency to buy energy from prosumers with excess energy in a microgrid system. The proposed method solves the problems associated with lack of trust in P2P energy trading and utilized the blockchain technology that made it impossible to tamper with data. The data is referred as transaction generated by using blockchain. A blockchain based smart contract execute the trading and payment rules without the intermediaries. Thus, the security and fairness of energy trading are significantly enhanced compared to conventional database technology. Without the third-party intervention, a miner that is selected among the participants in the microgrid environment process all the transaction generated from buying and selling energy. The smart contract consists of two main components; bidding and settlement module, payment module. The smart contract deployed in blockchain test network to test the interaction of smart contract. From the simulation, the proposed method is validated using realistic data with the Ethereum Virtual Machine (EVM). The method will be expected to be useful to designers who need to integrate renewable energy in a microgrid system.
A new end-to-end semi-supervised deep learning framework for mastering robot-written character identification

This paper studies the robot-written character identification problem under an end-to-end semi-supervised deep learning framework consisting of semi-supervised learning and deep learning modules. The learning framework allows a deep neural network to be trained on labeled and pseudo-labeled samples where pseudo-labeled samples refer to the samples with labels predicted by the semi-supervised learning module. Moreover, to guarantee the feasibility of the learning framework, a two-stage strategy is proposed for training the deep neural network. Specifically, the two-stage training strategy adopts pseudo-labeled samples firstly to train a deep neural network, then the deep neural network is refined using labeled samples one more time. As a result, more samples can be used for training a deep neural network, which is significant to the performance improvement of a deep neural network in the case of inadequate labeled samples. More importantly, the deep neural networks trained under the proposed learning framework perform better than the famous deep neural networks in a robot-written character identification experiment.
A deep learning approach to building a framework for Urdu POS and NER

The study examines various studies on Named Entity Recognition (NER) and Part of Speech (POS) tagging for the Urdu language conducted by academics and researchers. POS and NER tagging for Urdu still faces obstacles in terms of increasing accuracy while lowering false-positive rates and labelling unknown terms, despite the efforts of numerous researchers. In addition, ambiguity exists when tagging terms with different contextual meanings within a sentence. Due to the fact that Urdu is an inflectional, derivational, morphologically rich, and context-sensitive language, the existing models, such as Linguistic rule application, N-gram Markov model, Tree Tagger, random forest (RF) tagger, etc., were unable to produce accurate experimental results on Urdu language data. The significance of this study is that it fills a gap in the literature concerning the lack of POS and NER tagging for the Urdu language. For Urdu POS and NER tagging, we propose a deep learning model with a well-balanced set of language-independent features as well as a survey of important Urdu POS/NER techniques. In addition, this is the first study to use residual biDirectional residual Long short-term memory (residual biLSTM) architecture trained on the Urmono dataset in conjunction with the randomly initialised word2vec, fastText and mBERT embeddings are utilised to generate word or character vectors.For each experiment, the paper also employs the evaluation methods of Macro-F1, precision, precision, and recall. The proposed method with mbert embedding as word vectors provides best results of F1 score for POS and NER at 91.11% and 99.11% respectively. Also, the accuracy, precision and recall for POS are reported at 94.85%, 91.79% and 90.77%. Similarly, the accuracy, precision and recall for NER of the proposed model are reported at 99.77%, 98.78% and 99.45% respectively, which are higher than baseline models.
Security control of internet of things node access method using blockchain technology

Due to the problems of high false positive rate and low abnormal detection rate in the current IoT node access security control method, based on this, this paper designs a new IoT node access security control method. Firstly, the information security sharing structure is designed, the data collection work is completed, and the data is preprocessed. The preprocessed data is then shared and backed up through blockchain technology. Finally, take the login time, number of logins, login time, number of query results, query time and number of queries as the access constraints, and complete the security control of the access to the IoT nodes by correctly setting the access authorization conditions. The experimental results show that the false positive rate of IoT access behavior detection is always lower than 0.3%, the detection rate of IoT access anomaly is low, the detection sensitivity is higher than 80%, and the node encryption complexity is low, which has certain application value.
A hybrid deep learning for patient activity recognition (PAR): Real time body wearable sensor network from healthcare monitoring system (HMS)

Today’s healthcare sectors are driven and work to rescue patients as soon as possible by giving them the right care and treatment. A healthcare monitoring system works in two ways: by keeping track of the patient’s activities and overall health. For prompt treatment, such as giving the right and suitable medication, administering an injection, and providing additional medical help, nursing supervision is required. Wearable sensors are fixed or connected to the patient’s body and can follow their health. These IoT medical gadgets let clinicians diagnose patients and comprehend the processes from remote. However, the amount of data produced by IoT devices is so large that it cannot be handled manually. A model for automated analysis is required. Convolution Neural Network with Long-Short Term Memory (CNN-LSTM) was therefore suggested in this study as a Hybrid Deep Learning Framework (HDLF) for a Patient Activity Monitoring System (PAMS) that brings all healthcare activities with its classes. To incorporate medical specialists from all over the world and enhance treatment outcomes, the framework offers an advanced model where patient activities, health conditions, medications, and other activities are distributed in the cloud. An effective architecture for Wearable Sensor Network-based Human Action Recognition that combines neural network Simple Recurrent Units (SRUs) and Gated Recurrent Units (GRUs). For assessing the multimodal data input sequence, deep SRUs and a variety of internal memory states is utilized in this research. Furthermore, for addressing the concerns about accuracy oscillations or instability with decreasing gradients, a deep GRUs to store and learn the knowledge is conveyed to the future state. The analysis suggests that CNN-LSTM is then contrasted with some of the currently used algorithms, and it is found that the new system has a 99.53% accuracy rate. The difference between this accuracy result and the current value is at least 4.73%.
Decision-making under uncertainty for multi-robot systems

In this overview paper, we present the work of the Goal-Oriented Long-Lived Systems Lab on multi-robot systems. We address multi-robot systems from a decision-making under uncertainty perspective, proposing approaches that explicitly reason about the inherent uncertainty of action execution, and how such stochasticity affects multi-robot coordination. To develop effective decision-making approaches, we take a special focus on (i) temporal uncertainty, in particular of action execution; (ii) the ability to provide rich guarantees of performance, both at a local (robot) level and at a global (team) level; and (iii) scaling up to systems with real-world impact. We summarise several pieces of work and highlight how they address the challenges above, and also hint at future research directions.
Review Your Choices: When Confirmation Pages Break Ballot Secrecy in Online Elections

Abstract Online voting systems typically display a confirmation screen allowing voters to confirm their selections before casting. This paper considers whether a network-based observer can extract information about voter selections from the length of the exchanged network data. We conducted a detailed analysis of the Simply Voting implementation, which had randomly varying lengths of exchanged data due to dynamic page content and gzip compression. We demonstrated that we could correctly guess a voter’s selection with accuracy values ranging up to 100% in some instances. Even on more complex ballots, we generally could still rule out some combinations of candidates. We conducted a coordinated disclosure with the vendor and worked with them to roll out a mitigation. To their credit, this discovery (and therefore its fix) was made possible by their willingness to provide a publicly accessible demo, which, as we will show, remains a rarity in the industry.
Running the Race: A Swiss Voting Story

Abstract On the 29th of March 2019 the Swiss Federal Chancellery launched a review of the procedures surrounding e-voting after numerous flaws were discovered in the Scytl-Swiss Post system sVote. On the 5th of July 2021 an independent examination of the revised Swiss Post system began, with some cantons planning to launch new trials with this system. We summarize and reflect on our experience with the examination of the cryptographic protocol so far and muse over the future. We find that the protocol specification considerably improved over the last 3 years, both through changes in the protocol itself and through clarifications of missing elements in its specification. The clarifications also shed a new light on shortcomings of the protocol, in terms of both verifiability and privacy, including in the latest version of the system, which remains incompletely specified. We believe that these findings illustrate virtues of the examination requirements set by the Swiss Federal Chancellery: problems can be fixed before deployment rather than being exploited by malicious parties during an election. They also illustrate the tremendous challenges of creating a secure Internet voting system, and the long road ahead.