Reviewerly/exHarmony huggingface dataset:
Feature Engineering for Anomaly Detection and Classification of Blockchain Transactions

&lt;p&gt;The study analysed the importance of blockchain transaction features to identify suspicious activities. The feature engineering process involves exploiting domain knowledge, applying intuition, and performing a time-consuming series of trial-and-error extractions. Manually overseeing this process significantly impacts the performance of model generation. We address this challenge with an automated feature engineering approach to extract the various features from blockchain transactions. Also, we engineered a set of new features based on statistical measures and graph representation. We demonstrate that the proposed approach can be applied to various blockchain transaction datasets, including Bitcoin and Ethereum. The engineered features were tested against eight classifiers, including random forest, XG-boost, Silas, and neural network-based classifiers to identify the suspicious behaviour of transactions&lt;/p&gt;
Increasing photovoltaic system power output with white paint albedo – a scenario in Al-Mausaib City using PVSyst. software

&lt;p&gt;Sundry circumstances influence on the photovoltaic (PV) performance, &lt;br /&gt;a ground albedo one of an important factor that effect on the PV panel work. This research study is to improving the output solar structures fixed on horizontal of the buildings of the even surface with adjustable roof albedo; this study incorporates integrating innovative PV and levelheaded surface techniques to boost the energy production of photovoltaic system. The modeling and simulation study were utilized as part of the system to examine the effectiveness of the solar structure installed on concrete horizontal even tiles structures with a roof albedo of 0.25, After that, white paint was applied to the concrete surface in front of the solar system to raise the albedo to 0.70. The important findings demonstrate that a rise in surface albedo from 0.25 to 0.7 will contribute to an increase in yearly solar PV power generation. The case study scenario in Al-Mussaib Technical College (L at 32° 46′ 59.99″ N, 44° 19′ 0.01 E), this paper will study the effects of two different materials top surface on the yield of PV module, for these studies, the PVSyst. program version 7.2 is utilized to analysis data from PV solar stand-alone systems.&lt;/p&gt;
A Combined Approach of Sentimental Analysis Using Machine Learning Techniques

Sentiment analysis is a vital area of current research. The area of sentiment analysis is extensively used for observing text data and identifying the sentiment element. Every day, e- commerce sites produce a massive amount of text information from customer's comments, reviews, tweets, and feedbacks. One of the most recent technological advances in web development is the emergence of social networking websites. It aids in communication and knowledge gathering. Aspect - based evaluation of this information can help businesses to gain a greater understanding of their consumers' expectations and then shape their plans accordingly. It is difficult to convey the exact sentiment of a review. In this study, we demonstrated an approach that focuses on sentimental aspects of the item's characteristics. Consumer reviews on Amazon and IMDB have been presented and evaluated. We obtained the dataset from the UCI repository, where each analysis's opinion rates are first observed. To get meaningful information from datasets, and to eliminate noise, the pre-processing operations are performed by the system such as tokenization, punctuation, whitespace, special character, and stop-word removal. For the purpose of accurately representing the preprocessed data, feature selection methods such as word frequency-inverse document frequency are utilized (TF–IDF). The customer reviews from three datasets Amazon, Yelp, and IMDB is merged and classification is performed using classifiers such as Naïve Bayes, Random Forest, K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). In last, we provide some insight into the future text classification work.
PERANAN SISTEM INFORMASI MANAJEMEN DALAM PENGGUNAAN E-COMMERCE DAN KEMUDAHAN PELAYANAN KONSUMEN PT KERAMIKA INDONESIA ASSAOSIASI (KIA)

The emergence of the information age globally has resulted in a dynamic model shift in society. The field of technology is one of the characteristics of a modern society known as the information society. But not everyone is proficient in managing data. In other words, there are still some demographics who don't recognize the value of science. A new concept known as a Management Information System (MIS), which is characterized by the principles and various supporting features necessary to build an enterprise management information system, is developing in the information field as a reaction to this phenomenon.&#x0D; It goes without saying that wanting to expand a business can make it more profitable. A marketing strategy that utilizes electronic media E-commerce in particular is something that needs to be done. a system that makes it easy for users and customers to use services in buying and selling transactions. E-commerce, or electronic commerce as it is known in Indonesia, is the act of distributing, selling, buying and marketing something (goods and services) through the use of telecommunications networks such as the internet, television or other computer networks. E-commerce is the act of buying and selling products online. Over the past few years, e-commerce has expanded and eventually displaced traditional (offline) merchants.
Optimization of Breast Cancer Classification Using Faster R-CNN

Breast cancer results from aberrant cell division in the breast and leads to the formation of tumors.The modern lifestyle, which is instant and rarely exercises, is the main driving force for this disease.Therefore, this study aims to diagnose by recognizing the specific characteristics of cancer in a benign or malignant class in the breast area.This study approach uses the deep learning technology model Faster R-CNN and dataset Mammographic Image Analysis Society (MIAS).This model requires unique image characteristics to recognize and produce a higher accuracy value.Furthermore, this study proposes optimizing an image segmentation approach using Matlab, ImageJ, and Python software to enrich cancer-specific images.This approach plays a vital role in increasing the accuracy of cancer detection.The results of this study before optimization have an accuracy rate of 63.47% using a smartphone camera; after optimization, the highest accuracy value becomes 90.43%, therefore 9.57% requires further examination by a specialist.Based on these results, these results help assist radiologists in making decisions about the results of the initial examination of breast mammogram data.
Personality Classification Based on Textual Data using Indonesian Pre-Trained Language Model and Ensemble Majority Voting

Personality is a collection of striking traits and behaviors of a person.The use of personality models can be applied in employee recruitment systems or to analyze characteristics and potential in more depth.Personality models are usually made using psychological test data or filling out questionnaires.However, this requires a long time.Building a personality classification model using NLP and deep learning is considered one of the best solutions.However, the performance of the classification model still needs to be improved, especially for Indonesian Language data.So, this research makes a personality classification model with Indonesian Language data using BERT-based architectures such as Multilingual BERT, IndoBERT, and Indonesian RoBERTa Base with an ensemble majority voting technique.Data limitations and imbalances were addressed using synonym replacement by incorporating words from a pre-trained model, MBERT.Information contained in social media often has ambiguous meanings because the words conveyed are not standardized, so this study tries to retain the information contained in the text by translating emoticons and slang words at the preprocessing stage to help keep the meaning of words in context.The proposed approach's research results can improve the classification model's results in classifying personality.
Detection of Spam Email

Spam, often known as unsolicited email, has grown to be a major worry for every email user. Nowadays, it is quite challenging to filter spam emails since they are made, created, or written in such a unique way that anti-spam filters cannot recognize them. In order to predict or categorize emails as spam, this paper compares and reviews the performance metrics of a few categories of supervised machine learning techniques, including Svm (Support Vector Machine), Random Forest, Decision Tree, Cnn, (Convolutional Neural Network), Knn(K Nearest Neighbor), Mlp(Multi-Layer Perceptron), Adaboost (AdaptiveBoosting), and Nave Bayes algorithm. Thegoal of this study is to analyze the specificsor content of the emails, discover a limited dataset, and create a classification model that can predict or categorize whether spam is present in an email. Transformers’ Bidirectional Encoder Representations) has been optimized to perform the duty of separating spam emails from legitimate emails (Ham). To put the text’s context into perspective, Bert uses attention layers. Results are contrasted with a baseline Dnn (deep neural network) modelthat consists of two stacked Dense layers and a Bilstm (bidirectional Long Short-Term Memory) layer. Results are also contrasted with a group of traditional classifiers, including k- Nn (k-nearest neighbours) and Nb (Naive Bayes). The model is tested for robustness andpersistence using two open-source data sets, one of which is utilized to train the model.
Eye-Tracking in Virtual Reality

Eye-tracking is in our future. Across many fields, eye-tracking is growing in prominence. This paper focuses on eye-tracking in virtual reality as a case study to illuminate novel privacy risks and propose a governance response to them: a design shift that provides users with an experientially resonant means of understanding privacy threats. It is a strategy that Ryan Calo calls “visceral notice.” To make our case for visceral notice, we proceed as follows. First, we provide a concise account of how eye-tracking works, emphasizing its threat to autonomy and privacy. Second, we discuss the sensitive personal information that eye-tracking reveals, complications that limit what eye-tracking studies establish, and the comparative advantage large technology companies may have when tracking our eyes. Third, we explain why eye-tracking will likely be crucial for developing virtual reality technology. Fourth, we review Calo’s conception of visceral notice and offer suggestions for applying it to virtual reality to help users better appreciate eye-tracking risks. Finally, we consider seven objections to our proposals and provide counterpoints to them.
A Double Residual Iterative Regularization Method for SAR Image Despeckling

Synthetic aperture radar (SAR) imaging systems are greatly affected by speckle noise, which brings great difficulties to the postprocessing of SAR images. By characterizing edge and nonlocal self-similarity features simultaneously, this letter proposes a double residual iterative regularization (DRIR) despeckling method. To this end, the Fisher-Tippett (FT) distribution based despeckling model is first introduced. Next, to exploit the edge feature in a more reasonable way, a nonconvex total variation (NTV) regularization model based on FT distribution is proposed, and the solution to the resulting nonconvex optimization problem is given. Then, a despeckling model based on weighted Schatten <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> -norm is proposed, which can characterize the nonlocal self-similarity feature more flexibly. Finally, extensive experimental results demonstrate that the proposed method can effectively remove speckles while preserving edges and textures compared with some state-of-the-art methods.
Research and Implementation of Rowhammer Attack Method based on Domestic NeoKylin Operating System

Aiming at the problem of Disturbance Error of dynamic random access memory (DRAM) on domestic NeoKylin operating system, this article analyzes the reasons for rowhammer attacks, proposes a rowhammer attack scheme based on domestic NeoKylin operating system, and designs a tool to implement the attack. The results of the attack on domestic NeoKylin operating system demonstrate that this attack scheme can achieve bit flips by rapid and repeated access (hammer) to the neighboring rows of the target cell.
Video Person Re-identification Based on Transformer-CNN Model

To overcome the problems of pose variation, complex background and more occlusion in video person re-identification, a network model ResTNet based on convolutional neural network and Transformer was proposed. ResNet50 network was used to obtain local features and the output of its middle layer was input to Transformer as prior knowledge in ResTNet. In the Transformer branch, the size of the feature map was continuously reduced. The field of perception was expanded to fully explore the relationships among local features, and generated global features of pedestrians. The model computation was also decreased with the shift window method. Cross-entropy loss and triplet loss were used to optimize the model for the two branches during training, respectively. The Rank-1 and mAP on the large-scale MARS dataset reached 86.8% and 80.3%, respectively, which were 3.8% and 3.3% higher than the benchmark. The Transformer model was not only successfully applied to the field of video person re-identification, but also extensive experiments on several large datasets showed that the proposed ResTNet network can enhance the robustness of the recognition and improve the accuracy of person re-identification effectively.
Digital Innovation in Language Teaching—Analysis of the Digital Competence of Teachers according to the DigCompEdu Framework

The utilization of technology in the process of teaching and learning, as well as the influence of the COVID-19 crisis on education, are widely recognized and interconnected factors. This investigation is primarily focused on a group of formal education teachers who have received little attention to date, teachers from Official Language Schools, which are a part of the Special Regime Education system of Andalucía, a southern region of Spain, which provides foreign language education. Specifically, we aim to assess their level of digital proficiency in relation to their experience and use of information and communication technology (ICT) in the classroom. We also analyze how the March 2020 lockdown impacted their confidence levels in utilizing ICT in their teaching practices. One hundred and four teachers took part in the study and answered the DigCompEdu check-in questionnaire. The findings indicate that teachers’ overall self-assessment of their digital competence is low, with particular attention needed in the least developed areas, which is the facilitation of digital proficiency to students. Additionally, there are noteworthy differences in the variables of ICT experience and confidence. For example, the amount of time spent utilizing ICT in teaching does not necessarily correlate with teaching proficiency. Based on these results, we discuss potential strategies for enhancing digital competence in this educational group and propose some curriculum content for teacher training in digital competence.
Error Analysis of Written English Composition: A Case of Basic Level Students

Error analysis is an important branch of applied linguistics which deals with errors committed by second or foreign language learners. The present article is about error analysis that aims to investigate the frequency, types and causes of errors committed by basic level students. To accomplish the objectives, the researcher adopted positivist philosophical approach and survey research design. The researcher collected the data from two schools of Palpa and two schools of Rupandehi consisting ten students from each school. Writing composition was the main tool for data collection and the collected data were categorized into different categories such as verb tense, subject verb agreement, inappropriate use of article, wrong use of preposition. The frequency of occurrence of errors and their causes were analyzed. The findings of the study highlight that the most of basic level students commit grammatical errors in sub-verb agreement and they are caused due to intralingual as well as interlingual factors. This study can play a pivotal role for improving writing skills of students.
WRITING EFFECTIVE MULTIPLE-CHOICE QUESTIONS IN MEDICAL EDUCATION

Appears in: INTED2023 Proceedings Publication year: 2023Pages: 1-10ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.0003Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain
Paint by Word

We investigate the problem of zero-shot semantic image painting. Instead of painting modifications into an image using only concrete colors or a finite set of semantic concepts, we ask how to create semantic paint based on open full-text descriptions: our goal is to be able to point to a location in a synthesized image and apply an arbitrary new concept such as "rustic" or "opulent" or "happy dog." To do this, our method combines a state-of-the art generative model of realistic images with a state-of-the-art text-image semantic similarity network. We find that, to make large changes, it is important to use non-gradient methods to explore latent space, and it is important to relax the computations of the GAN to target changes to a specific region. We conduct user studies to compare our methods to several baselines.
A review on object detection for autonomous mobile robot

The advancement of autonomous mobile robots (AMR) is vastly being discovered and applied to several industries. AMR contributes to the development of artificial intelligence (AI), which focuses on the growth of human-interaction systems. However, it is safe to understand that mobile robots work closely in real-time and under changing surroundings. Similarly, some limitations may affect the efficiency of mobile robots. Thus, to improve the system's efficiency and accuracy, mobile robots should adopt the ability to detect incoming obstacles accurately. This paper presents the findings of a brief technology review aimed at identifying the current state of the art and future needs for AMR in object detection. This review paper is presented in the form of a narrative-literature review. Review articles were collected from 2015 until 2022 from journals or conference papers from well-known sources like IEEE Xplore, Science Direct, Scopus, and Web of Science (WOS). The analysis of the articles was discussed in four main topics, AI, object detection, AMR, and deep learning.
High performance of optimizers in deep learning for cloth patterns detection

In Deep learning, optimization methods are an essential role. Optimizers are used to change weights and learn rates to reduce or minimize losses in a neural network. Nowadays, Deep Learning is widely used, especially in object detection tasks. In this case, cloth patterns are considered for object detection and assist visually impaired people. The visually impaired person had many limitations when doing their activity, not least when choosing clothes; it would be difficult without guidance or tools like Braille labels. In this research, a system was researched to detect 11 different cloth patterns (Argyle, Batik, Camouflage, Gingham, Dotted, Floral, Leopard, Solid, Striped, Zebra, and Zigzag) using RetinaNet with ResNet-152 architecture. To achieve the best model performance, compare 6 optimizers, such as Stochastic Gradient Descent, Root Mean Square Propagation, Adaptive Moment Estimation, Adaptive Delta, Adaptive Norm, and Adaptive Gradient was conducted. Each optimizer has trained with three different learning rates (1E-3, 1E-4, and 1E-5). A model with Adamax optimizer and learning rate 1E-4 was achieved with the highest accuracy with mAP (mean Average Precision) 91.28% during the training process. Based on the testing result this model was achieved precision 93.01%, recall 92.91%, F1-Score 92.79%, and accuracy 92.91%.
Low-Pass Filter Based on Base Differential and Differential Difference Operational Amplifiers

A new low-pass filter (LPF) circuit with independent adjustment of various, incl. digitally controlled, pole frequency resistors, pole quality factor and transmission coefficient. The proposed low-pass filter is based on the use of the properties of a multi-differential operational amplifier that performs the functions of a signal adder. The peculiarity of the filter is that it has three inputs, with respect to which different transmission coefficients are implemented, incl. inverting (-1) and non-inverting (+1). To check these properties of a low-pass filter in the Micro-Cap environment, computer simulation of a specific circuit was performed on a multi-differential operational amplifier AD 8130. Mathematical expressions are given for the main parameters of the proposed low-pass filter, which allow parametric synthesis of elements of a specific circuit under given restrictions on the used element base.
Farspredict: A benchmark dataset for link prediction

Link prediction with knowledge graph embedding (KGE) is a popular method for knowledge graph completion. Furthermore, training KGEs on non-English knowledge graph promote knowledge extraction and knowledge graph reasoning in the context of these languages. However, many challenges in non-English KGEs pose to learning a low-dimensional representation of a knowledge graph's entities and relations. This paper proposes "Farspredict" a Persian knowledge graph based on Farsbase (the most comprehensive knowledge graph in Persian). It also explains how the knowledge graph structure affects link prediction accuracy in KGE. To evaluate Farspredict, we implemented the popular models of KGE on it and compared the results with Freebase. Given the analysis results, some optimizations on the knowledge graph are carried out to improve its functionality in the KGE. As a result, a new Persian knowledge graph is achieved. Implementation results in the KGE models on Farspredict outperforming Freebases in many cases. At last, we discuss what improvements could be effective in enhancing the quality of Farspredict and how much it improves.