Reviewerly/exHarmony huggingface dataset:
Illicit Activity Detection in Bitcoin Transactions using Timeseries Analysis

A key motivator for the usage of cryptocurrency such as bitcoin in illicit activity is the degree of anonymity provided by the alphanumeric addresses used in transactions.This however does not mean that anonymity is built into the system as the transactions being made are still subject to the human element.Additionally, there is around 400 Gigabytes of raw data available in the bitcoin blockchain, making it a big data problem.HPCC Systems is used in this research, which is a data intensive, open source, big data platform.This paper attempts to use timing data produced by taking the time intervals between consecutive transactions performed by an address and make an --von Mises criterion, two addresses are compared to find if they are from the same source.The BABD-13 dataset was used as a source of illegal addresses, which provided both references and test data points.The research shows that time-series data can be used to represent transactional behaviour of a user and the algorithm proposed is able to identify different addresses originating from the same user or users engaging in similar activity.
Question Classification in Albanian Through Deep Learning Approaches

In recent years, there is growing interest in intelligent conversation systems.In this context, Question Classification is an essential subtask in Question Answering systems that determines the question type, therefore, also the type of the answer.However, while there is abundant research for English, little research work has been carried out for other languages.In this paper we deal with classification of questions in the Albanian language which is considered a complex Indo-European language.We employ both machine learning and deep learning approaches on a large corpus in Albanian based on the six-class TREC dataset with approximately 5000 questions.Experiments with and without stop-words show that the impact of stop-words is significant in the accuracy of the classifier.Extensive comparison of algorithms for the task of question classification in Albanian show that deep learning algorithms outperform conventional machine learning approaches.To the best of our knowledge this is the first approach in literature for classifying questions in Albanian and the results are highly comparable to English.
Pancreatic Cancer Segmentation and Classification in CT Imaging using Antlion Optimization and Deep Learning Mechanism

Pancreatic cancer, a fatal type of cancer, has a very poor prognosis.To monitor, forecast, and categorise cancer presence, automated pancreatic cancer segmentation and classification utilising Computer-Aided Diagnostic (CAD) model can be used.Furthermore, deep learning algorithms can provide in-depth diagnostic knowledge and precise image analysis for therapeutic usage.In this context, our study aims to develop an Antlion Optimization-Convolutional Neural Network-Gated Recurrent Unit (ALO-CNN-GRU) model for pancreatic tumor segmentation and classification based on deep learning and CT scans.The ALO-CNN-GRU technique's objective is to segment and categorize the presence of cancer tissues.This technique consists of pre-processing, segmentation and feature extraction and classification phases.The images go through a preprocessing stage to reduce noise from the dataset that was obtained.A hybrid Gaussian and median filter is applied for the pre-processing phase.To identify the pancreatic area that is affected, the segmentation is processed utilizing the Antlion optimization method.Then, the categorization of pancreatic cancer as benign or malignant is done by employing the classifiers of the Convolutional neural network and Gated Recurrent Unit networks.The suggested model offers improved precision and a better rate of pancreatic cancer diagnosis with an accuracy of 99.92%.
A Practical-Quantum Differential Attack on Block Ciphers

Abstract Differential attack is a basic cryptanalysis method for block ciphers that exploits the high probability relations between the input and output differences. The existing work in quantum differential cryptanalysis of block ciphers is focused on resource estimation to recover the last round subkeys on the basis of relations constructed using classical methods. To find such relations using quantum computing, we propose a method to search the high probability differential and impossible differential characteristics. The method explores all possible input and output difference pairs using superposition of qubits. The proposed method is used to design the quantum circuit to search the differential characteristics for a toy cipher smallGIFT. We execute the quantum circuit on a quantum simulator to get differential and impossible characteristics. These characteristics are validated with the characteristics obtained using branch-and-bound based method. The differential characteristic is used to mount quantum key recovery attack using Grover's search. We provide the estimation of quantum resources to search the differential characteristics of lightweight block cipher GIFT-64 on a quantum computer.
Customer sentiment analysis for Arabic social media using a novel ensemble machine learning approach

&lt;span lang="EN-US"&gt;Arabic’s complex morphology, orthography, and dialects make sentiment analysis difficult. This activity makes it harder to extract text attributes from short conversations to evaluate tone. Analyzing and judging a person’s emotional state is complex. Due to these issues, interpreting sentiments accurately and identifying polarity may take much work. Sentiment analysis extracts subjective information from text. This research evaluates machine learning (ML) techniques for understanding Arabic emotions. Sentiment analysis (SA) uses a support vector machine (SVM), Adaboost classifier (AC), maximum entropy (ME), k-nearest neighbors (KNN), decision tree (DT), random forest (RF), logistic regression (LR), and naive Bayes (NB). A model for the ensemble-based sentiment was developed. Ensemble classifiers (ECs) with 10-fold cross-validation out-performed other machine learning classifiers in accuracy (A), specificity (S), precision (P), F1 score (FS), and sensitivity (S).&lt;/span&gt;&lt;p&gt; &lt;/p&gt;
Letter From the President [President’s Message]

Hello and nice to meet you! My name is Mariko Burgin and I am the incoming IEEE Geoscience and Remote Sensing Society (GRSS) president. You can reach me at president@ieee-grss.org and @GRSS_President on Twitter.
Accelerating Social Science Knowledge Production with the Coordinated Open-Source Model

With the growing complexity of knowledge production, social science must accelerate and open up to maintain explanatory power and responsiveness. This requires redesigning the front-end of research to build an open and expandable knowledge infrastructure that stimulates broad collaborations, enables breaking down inertia and path dependencies of conventional approaches, and boosts discovery and innovation. This article discusses the coordinated open-source model as a promising organisational scheme that can supplement conventional research infrastructure in certain areas. The model offers flexibility, decentralization, and community-based development and aligns with open science ideas, such as reproducibility and transparency. Similar solutions have been successfully applied in natural science, but social science lags behind. I present the model's design, and consider its potential and limitations (e.g., regarding development, sustainability and coordination). I also discuss open-source applications in various areas, including the first open-source survey harmonization project in social science.
Setting the Tone for Sound Forensic Investigations on Android-Based Social Media Platforms

Abstract: Android Social Media Applications have become a yardstick in facilitating a platform for human socialization on cyber space. They are an inevitable alternative, which is fast replacing most traditional ways that lacks full multimedia interaction adored by many. These applications are of forensic value as they account for most activities helpful in either incriminating or exonerating suspects in cases of adverse events. By default, most social applications store activity data in specific directories they create at the background of the hosting Android devices. Through expertise, this data can be extracted and analyzed to come up with meaningful insights useful in an inquiry of digital evidence interest. This study focused on forensics of Twitter and Clubhouse android based social media applications. The approach taken was to install these applications on emerging Android devices using the Samsung Galaxy S20+ (SMGS20+) and Samsung Galaxy Tab A7 (SMGTA7), populate known test data, perform data acquisition, execute data analysis noting results and then do a comparative analysis of tools and techniques utilized towards provisioning alternative solutions.
Digital Literacy Ability of Students of Biology Education Study Program FSTT Undikma

This study aims to understand the condition of digital literacy skills of S1 students of the biology education study program, Faculty of Science, Engineering and Applied (FSTT) Mandalika University of Education (Undikma). Cross-sectional study with a survey method that aims to analyze variable data collected at a certain point in time throughout the sample population or a predetermined subset. The population of this study is all active Biology Education study program students as many as 212 students. The research sample amounted to 86 students as respondents obtained by accidental sampling techniques. This research instrument is in the form of a google form-based student digital literacy mastery questionnaire, which is given online. The results of the study on each indicator are the Access indicator of 58.1% of students' goals to access the internet to complete assignments, 91.9% using mobile phone media, 40% using subjects as search points, and 67% using complete search keywords to access information from the internet. In the Analyze and Evaluate indicator, 60.5% read and understand the information thoroughly and 48.98% read the information thoroughly and the sources used. In the Create indicator, the new form of information created by students is with 41.9% making research reports, 36.0% using Microsoft Office devices, and 51.2% in the form of writing. The Reflect indicator is the largest percentage of respondents to respect and maintain a code of ethics when accessing information accessed from the internet with a magnitude of 65.1% listing the source of information cited appropriately and 75.6% ensuring the correctness of the information and using as needed. Indiator Act, the actions of college students after accessing and using the internet with 46.5% using information to solve problems and 34.9% to make presentations in public. Based on this description, it can be concluded that students' digital literacy skills have reached the good category
HOW TO IMPROVE WEB APPLICATION DEVELOPMENT USING A LAYERED REFERENCE MODEL AND QUALITY INDICATORS

This project proposes the use of a reference model for the software development process based on a Web architecture, this allows the evaluation of the applications developed using different Web frameworks, which are mostly used in IT industry in the region.Through the application of a survey, a comparison of these tools is made using the reference model itself as well as the good practices in the Web development processes that companies use.The characteristics of the tools used allow to be establishing a series of criteria that must be met for the software development process with quality criteria that comply with widely accepted standards.The comparison is made following a research methodology; an analysis-synthesis of the different tools for the development of Web applications in the region was carried out, an exploratory research was also implemented to establish good practices and finally the tests of the most used frameworks.
THE IMPLEMENTATION OF PROBLEM BASED LEARNING ACTIVITY THROUGH ONLINE SCHOOL FIELD PRACTICE

The application of problem-based learning is a style of methodology that puts the idea of learning from facts and looking at real-world problems into practice. In order to examine mediated communication, this study used a qualitative ex experimental design using qualitative techniques. This method, known as the "qualitative experiment," makes use of in-depth interviews and focus groups under randomized stimulus circumstances that are commonly found in experimental research. In this work, observational data collecting and document analysis were the primary methods of data acquisition. This study intends to examine how problem-based learning is used in online English learning activities. Highlights from the approach's implementation initiatives include: 1) The concept grasp of the student regarding the subject matter; and 2) the student's critical thinking. These findings suggest that the qualities of the Problem Based Learning approach, namely the student's independence and social competency, had emerged.
Deep Learning and Transfer Learning Methods to Effectively Diagnose Cervical Cancer from Liquid-Based Cytology Pap Smear Images

As cervical cancer is considered one of the leading causes of death for women globally, different screening techniques have emerged. As the Papanicolaou technique generates high numbers of false negatives due to only testing 20% of a sample, the liquid-based cytology technique was developed to test 100% of the sample and improve accuracy. However, as the larger sample size has made it difficult to detect the lesion images through a microscope, studies have looked for ways to intelligently analyze sample. The aim of this study is to develop an artificial intelligence image recognition system that detects the lesion level of cervical cancer of liquid-based Pap smears under the Bethesda classification of cancer (NI/LSIEL/HSIEL/SCC). For this purpose, six activities were carried out: dataset selection, data augmentation, optimization, model development, evaluation and system construction. A dataset built from publicly available Pap smear images and passed through data augmentation algorithms generated a total of 2,676 images. Two models, ResNet50V2 and ResNet101V2, were developed under Deep Learning and Transfer Learning protocols. The evaluation showed that the ResNet50V2 model obtained better performance, where the classification of HSIL and SCC type images obtained a precision of 0.98 and achieved an accuracy of 0.97. Finally, the system based on the ResNet50V2 model was built and its performance was validated.
Generation of Pseudo-Random Quantum States on Actual Quantum Processors

The generation of a large amount of entanglement is a necessary condition for a quantum computer to achieve quantum advantage. In this paper, we propose a method to efficiently generate pseudo-random quantum states, for which the degree of multipartite entanglement is nearly maximal. We argue that the method is optimal, and use it to benchmark actual superconducting (IBM’s ibm_lagos) and ion trap (IonQ’s Harmony) quantum processors. Despite the fact that ibm_lagos has lower single-qubit and two-qubit error rates, the overall performance of Harmony is better thanks to its low error rate in state preparation and measurement and to the all-to-all connectivity of qubits. Our result highlights the relevance of the qubits network architecture to generate highly entangled states.
Clinical Text Classification with Word Representation Features and Machine Learning Algorithms

Clinical text classification of electronic medical records is a challenging task. Existing electronic records suffer from irrelevant text, misspellings, semantic ambiguity, and abbreviations. The approach reported in this paper elaborates on machine learning techniques to develop an intelligent framework for classification of the medical transcription dataset. The proposed approach is based on four main phases: the text preprocessing phase, word representation phase, features reduction phase and classification phase. We have used four machine learning algorithms, support vector machines, naïve bayes, logistic regression and k-nearest neighbors in combination with different word representation models. We have applied the four algorithms to the bag of words, to TF-IDF, to word2vec. Experimental results were evaluated based on precision, recall, accuracy and F1 score. The best results were obtained with the combination of the k-NN classifier, and the word represented by Word2vec achieving an accuracy of 92% to correctly classify the medical specialties based on the transcription text.
comparison of small sample methods for Handshape Recognition

Automatic Sign Language Translation (SLT) systems can be a great asset to improve the communication with and within deaf communities. Currently, the main issue preventing effective translation models lays in the low availability of labelled data, which hinders the use of modern deep learning models.&#x0D; SLT is a complex problem that involves many subtasks, of which handshape recognition is the most important. We compare a series of models specially tailored for small datasets to improve their performance on handshape recognition tasks. We evaluate Wide-DenseNet and few-shot Prototypical Network models with and without transfer learning, and also using Model-Agnostic Meta-Learning (MAML).&#x0D; Our findings indicate that Wide-DenseNet without transfer learning and Prototipical Networks with transfer learning provide the best results. Prototypical networks, particularly, are vastly superior when using less than 30 samples, while Wide-DenseNet achieves the best results with more samples. On the other hand, MAML does not improve performance in any scenario. These results can help to design better SLT models.
Text Summarizing and Clustering Using Data Mining Technique

Text summarization is an important research topic in the field of information technology because of the large volume of texts, and the large amount of data found on the Internet and social media. The task of summarizing the text has gained great importance that requires finding highly efficient ways in the process of extracting knowledge in various fields, Thus, there was a need for methods of summarizing texts for one document or multiple documents. The summarization methods aim to obtain the main content of the set of documents at the same time to reduce redundant information. In this paper, an efficient method to summarize texts is proposed that depends on the word association algorithm to separate and merge sentences after summarizing them. As well as the use of data mining technology in the process of redistributing information according to the (K-Mean) algorithm and the use of (Term Frequency Inverse Document Frequency TF-IDF) technology for measuring the properties of summarized texts. The experimental results found that the summarization ratios are good by deleting unimportant words. Also, the method of extracting characteristics for texts was useful in grouping similar texts into clusters, which makes this method possible to be combined with other methods in artificial intelligence such as fuzzy logic or evolutionary algorithms in increasing summarization rates and accelerating cluster operations.
Exploring mistake of language discourse that arises when learners of Indonesian for Foreign Speakers (BIPA) Fatoni University

The purpose of this study is to describe the causes and forms of language errors at the phonological level of BIPA students. This type of research is qualitative research. The method used in this research is descriptive analysis method. The data taken in this study is a form of language error at the phonological level in the form of words spoken by BIPA 1-2 students at Fatoni University Thailand. So. Meanwhile, the data sources in this study were BIPA 1-2 students at Fatoni University, totaling 8 people. The instruments in this study were researchers using laptops and zoom applications and assisted with data inventory formats. Data collection techniques are carried out by observation assisted by recording techniques, observing techniques and note-taking techniques. The technique used in checking the validity of the data in this study is the triangulation technique. The results of data analysis can be concluded several things. First, the forms of language errors found by BIPA students at Fatoni University Thailand at the phonological level are, a) phoneme change errors. b) phoneme omission errors, and c) phoneme additions. Second, the factors causing BIPA students to experience language errors in speaking skills are influenced by factors, first, namely factors influenced by the language they master first. Second, the lack of understanding of the language users of the language they use, many do not understand good and correct Indonesian, both in meaning and in pronunciation or pronunciation.
CHAD for Expressive Total Languages

We show how to apply forward and reverse mode Combinatory Homomorphic Automatic Differentiation (CHAD) to total functional programming languages with expressive type systems featuring the combination of - tuple types; - sum types; - inductive types; - coinductive types; - function types. We achieve this by analysing the categorical semantics of such types in $\Sigma$-types (Grothendieck constructions) of suitable categories. Using a novel categorical logical relations technique for such expressive type systems, we give a correctness proof of CHAD in this setting by showing that it computes the usual mathematical derivative of the function that the original program implements. The result is a principled, purely functional and provably correct method for performing forward and reverse mode automatic differentiation (AD) on total functional programming languages with expressive type systems.
SentiMLBench: Benchmark Evaluation of Machine Learning Algorithms for Sentiment Analysis

Sentiment Analysis has been a topic of interest for researchers due to its increasing usage by Industry. To measure end-user sentiment., there is no clear verdict on which algorithms are better in real-time scenarios. A rigorous benchmark evaluation of various algorithms running across multiple datasets and different hardware architectures is required that can guide future researchers on potential advantages and limitations. In this paper, proposed SentiMLBench is a critical evaluation of key ML algorithms as standalone classifiers, a novel cascade feature selection (CFS) based ensemble technique in multiple benchmark environments each using a different twitter dataset and processing hardware. The best trained ensemble model with CFS enhancement surpasses current state-of-the-art models, according to experimental results. In a study, though ensemble model provides good accuracy, it falls short of neural networks accuracy by 2%. ML algorithms accuracy is poor as standalone classifiers across all three studies. The supremacy of neural networks is further stamped in study three where it outperforms other algorithms in accuracy by over 10%. Graphical processing unit provide speed and higher computational power at a fraction of a cost compared to a normal processor thereby providing critical architectural insights into developing a robust expert system for sentiment analysis.