Reviewerly/exHarmony huggingface dataset:
Effective Generative Data Augmentation in Condition Monitoring

&lt;p&gt;In this paper, we investigate the effectiveness of a diffusion model to generate an augmented labeled dataset of synthetic images. The creation of synthetic images becomes indispensable in condition monitoring applications where capturing a diverse range of damages for training a supervised deep-learning model is impracticable or infeasible. To evaluate the potential of this technique, we explore the case of damage classification in synthetic fiber ropes. The approach involves training and fine- tuning a stable diffusion model to create synthetic photorealistic images, representing three distinct types of damages commonly found in fiber ropes. We assessed the efficacy of our method by training and testing the Resnet50 classifier on the synthetic dataset. Our results show that the classifier’s performance remains stable when up to 75% of the images used for training are synthetically generated. This outcome suggests that synthetically generated images produced by a finely-tuned diffusion model possess sufficient quality to serve as a viable addition to real images in data augmentation.&lt;/p&gt;
Blockchain Enterprise Architecture: Monolith or Microservices in the Financial Industries

&lt;p&gt;As blockchain technology gains prominence in the financial sector, the architectural frameworks guiding its integration play a crucial role in determining operational efficacy. This research delves into the architectural juxtaposition of monolithic versus microservices approaches, specifically within the context of blockchain applications tailored for financial enterprises. By analyzing the distinct attributes of these architectures, the study sheds light on their practical advantages and challenges, especially concerning scalability, security, transactional integrity, and seamless integration with existing financial systems. Drawing from real-world blockchain implementations in banking, trading, and insurance, the research provides actionable insights into the architectural intricacies that dictate system robustness, adaptability, and long-term viability. The ultimate goal of this exploration is to furnish financial institutions with pragmatic guidance, facilitating architectural decisions that resonate with their immediate operational needs and strategic aspirations. &lt;/p&gt;
Deep learning with Hyperparameter optimisation for SOC estimation of LFP cells

&lt;p&gt;Deep learning with Hyperparameter optimisation for SOC estimation of LFP cells&lt;/p&gt;&lt;p&gt;This work has been submitted to the IEEE (OA) for possible publication. Copyrights may be transferred without notice, after which this version may no longer be accessible.&lt;/p&gt;
High and Low-Level Classification based Features for Content-based Image Retrieval in Privacy-Preserving

Abstract The authors have requested that this preprint be removed from Research Square.
A Survey of Deepfake Detection Methods

In this work, we examine recent methods employed for detecting deepfake images and videos, which have become increasingly prevalent in recent times. Deepfake datasets are categorized into generations based on the number of data, quality, and diversity of the data they contain, and a new realistic dataset has been generated to accurately reflect real-world conditions. In this paper, we evaluate the methodologies used for deepfake detection using computer vision and deep learning on currently available datasets and present the results in a comprehensive manner.
Deep Active Learning for Address Parsing Tasks with BERT

Deep learning models tend to perform better with larger datasets. With decreasing data handling costs, researchers have the means to gather and store vast amounts of unlabeled data. Supervised learning, on the other hand, requires training data to be labeled by annotators. However, high annotation costs pose challenges to labeling an optimum portion of the available data. One proposed method to mitigate this problem is to employ active learning (AL). AL strategies use a machine learning model to select the most informative and representative samples among unlabeled data points. Here, we demonstrate the effectiveness of uncertainty-based active learning strategies, including a new strategy, for address parsing with a BERT model on an in-house Arabic address dataset manually annotated for two different tasks. We compare AL methods with random sampling and longest-sentence baselines. We show that AL strategies' usefulness greatly depends on dataset characteristics, being less effective on datasets with fewer classes. We conclude that AL for address parsing with BERT decreases annotation costs, if measured in the number of queries. Yet, due to AL methods' tendency to select longer queries, some strategies may increase labeling costs, measured in the total number of words.
Lattice Codes for CRYSTALS-Kyber

&lt;p&gt;This letter describes a lattice encoder for the NIST-recommended post-quantum encryption algorithm: Kyber. The key idea is to refine the analysis of Kyber decoding noise. We prove that Kyber decoding noise can be bounded by a sphere. This result shows the Kyber encoding problem is essentially a sphere packing in a hypercube. Lattice codes are then constructed to ensure denser packing and a lower decryption failure rate (DFR). Compared with the original Kyber encoder, the communication cost is reduced by up to 32.6%, the DFR is reduced by a factor of up to 2^{85}. The security arguments remain the same.&lt;/p&gt;
A Comparative Study of Student Selection Systems in Korean and American Teacher Training Universities Focusing on Music Teachers

This study aims to enhance the system for nurturing elementary school music teachers in South Korea through a comparative analysis with the United States, focusing on the student selection methods and curricular approaches in teacher training universities. By examining the differences between the two countries, particularly in relation to the admission process and the content of music education programs, this research seeks to identify potential areas for improvement in South Korea's teacher preparation system. The findings reveal significant differences between the two countries in their approach to selecting pre-music teachers. All 10 universities of education in Korea do not require applicants to demonstrate prior musical knowledge or proficiency in playing musical instruments for admission into the music education department. In contrast, the music education departments of the 10 universities in the United States mandate auditions, indicating a higher level of preparedness expected from applicants. It shows that minimum preparation to become a music teacher, handling musical instruments, basic musical knowledge has a great impact on teaching music and the teachers’ own efficacy.
Game night: Tabletop scicomm and Dorito dupes

Dungeons & Dragons & dipoles In August 2021, Skyler Ware gathered half a dozen players at a game store in Pasadena, California, for a 3 h “one shot” game of Dungeons and Dragons (D&D). Some had played D&D before, some hadn’t. But they were all there to get a side of science with their magical adventures. Ware, a graduate student at the California Institute of Technology who studies battery electrolyte materials, had inserted elements of her research into the plot of the role-play adventure she had planned. At the beginning of the session, the players found themselves stuck in a dungeon. They each had a battery-powered teleportation device from which the batteries had been stolen. Participants had to search their surroundings to find replacements, fighting off monsters in the process. The party eventually found a stockpile of batteries with a variety of electrolyte materials: solid, liquid, and gel. Ware provided
On the Double Descent effect in Federated Learning

&lt;p&gt;This paper investigates the occurrence of the double descent phenomenon within the domain of federated learning. It derives a closed-form solution to calculate the mean excess risk in the coefficients of a linear regression model, with the theoretical findings verified through simulations. The results confirm the presence of the double descent effect in the case of federated learning. This effect is especially pronounced in a heterogeneous scenario where local datasets of collaborating users have different sizes and properties. Furthermore, the results unveil unexpected and non-trivial performance behaviors. Interestingly, federated learning even outperforms centralized learning in particular scenarios when the expected excess risk of the former is lower than that of the latter. Finally, our analysis reveals that the typically used random user selection in federated learning exhibits asymptotically lower performance in contrast to the case of excluding only a single carefully chosen user from the learning process.&lt;/p&gt;
Structured and Unstructured Speech2Action Frameworks for Human-Robot Collaboration: A User Study

&lt;p&gt;This research delves into user preferences concerning structured (the subject follows an exact script to command the robot) and unstructured (the subject commands the robot in a conversational way) robot interaction through natural spoken language. Data was gathered from 30 adult participants who completed two distinct tasks involving both structured and unstructured commands. The study examines correlations between robot errors and user perceptions, as well as how past or present failures impact participants' perception of robot utility. Three hypotheses are formulated, and the paper offers a comprehensive overview of the study's aims, methodologies, and principal findings, which were ascertained using paired t-Test and Kendall-Tau correlations. The study indicates that participants showed a preference for the unstructured task in contrast to the structured one. Analysis of the data revealed interesting correlations between the user perception of the robot and the robot errors. &lt;/p&gt; &lt;p&gt;&lt;em&gt;This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible&lt;/em&gt; &lt;/p&gt;
Spatial and temporal variability of Cadmium and Lead in Urban Soils of Thessaloniki (northern Greece).

In this study, an investigation was carried out to record, document and monitoring the pollution caused by heavy metals in soil surface soils in the urban fabric of the city of Thessaloniki. During the years 2021 &amp; 2022, surface soil samples were collected in the spring and autumn of each year. Soil and chemical analyses were conducted to determine the physicochemical properties of soil samples. The available and pseudo-total concentration of cadmium (Cd) and lead (Pb) was determined and the mean values for the two years of the study were calculated. Using Geographical Information Systems (GIS), thematic maps were built in order to study the spatial and temporal distribution of each metal. The statistical tools used should provide useful guidance for pollution surveillance and identification of potential sources of pollution in the study area.
Solar attenuation due to particulate matter in arid environments

The depletion of solar irradiation has several causes such as clouds and suspended particulate matters in the atmosphere. Particulate matters play an important role in decreasing solar irradiation by blocking partly the solar energy from reaching the ground. This lost irradiation must be taken into consideration when planning large installations of PV panels’ fields along with other environmental factors. The blocking effect of PMs is higher in places where the density of PMs is higher due to geographical location or the lack of precipitation. The main objective of this work is to study experimentally the impact of particulate matters of different sizes (PM2.5, PM10, and TSP), measured at ground-level, on global horizontal irradiation (GHI) in arid climates. The measurements were taken over a six months period, from May to October 2022. The city of Riyadh, Saudi Arabia, was chosen to represent arid conditions due to its high suspended particulates density and the lack of precipitation. The PM measurements were recorded every minute from dawn to dusk and were processed to obtain a correlation between the measured PM values and the actual GHI. In addition, a comparison between the GHI obtained by the developed model and the one obtained from ASHRAE clear sky model. During the period of the experiment, the measurements showed that the ASHRAE clear sky model overestimated the amount of solar energy reaching the ground by over 10%.
Continuous Bank Audit Using Internet of Things

The Internet of Things (IoT) is a network of interconnected devices such as computers, wireless sensors, cameras, smart mobiles, smart fire alarms, smart door locks, smart security systems, etc. The IOT capability of enabling capture of real-time data for assessment, interpretation and analysis makes it suitable for continuous auditing of banks. Internal audit of banks has to ensure that there are no errors or frauds including cyber frauds. Statutory audit as part of regulatory oversight has to ensure that the financial statements are true and fair while reporting to multiple stakeholders. IOT makes it possible to collect, collate, and analyze real time data from multiple devices used by the customers. This makes it crucial for real time as well as post facto monitoring, which is a major responsibility of audit as a part of the governance function and regulatory validation and oversight. The current research lacks a systematic approach towards application of IOT to bank audit. This study proposes a conceptual framework using IOT for the continuous ubiquitous audit which will transform bank audits integrating internal and external audit.
Brain MRT image super resolution using discrete cosine transform and convolutional neural network

High Resolution (HR) images have numerous applications, such as video conferencing, remote sensing, medical imaging, etc.Furthermore, a few challenges with the super resolution algorithms of magnetic resonance brain images are now obtainable, namely, low sensitivity, significant frequency noise as well as poor resolution.To fix these problems, a Convolutional Neural Network (CNN) based Discrete Cosine Transform (DCT) singular frame quality improvement method is described.There are two stages in this proposed method, involving training and testing.During the training stage, the HR, and Low Resolution (LR) pictures are employed as input, and they are preprocessed to create blocks of images.The histogram and DCT are used for extracting the features from the LR and HR blocks, and these extracted features are assigned with class id.The CNN, which extracts the features and allocates class id, receives its feature extractor as its final input.An LR input image is once more divided into [2 × 2] blocks during the testing stage, so each block histogram and DCT feature are estimated.Each feature vector is fed into the neural network as well as the results are contrasted with a set of feature vectors that have been recorded, in addition to the class id that has been allocated to a certain vector.In order to generate a Super resolution image with an LR image, a relevant HR block is then swapped out for this LR block.These results indicated that the initial dataset can achieve 22.4 and 19.5 Peak Signal to Noise Ratio (PSNR) and Root Mean Square Error (RMSE) values while measuring the effectiveness of this proposed method using RMSE and PSNR.Then, the second dataset illustrates that the PSNR and RMSE values are 20.1 and 25.5.For the third dataset, the values are 45.7 and 12.3, respectively.However, the presented method works better than the neural method of Super Resolution Channel Spatial Modulation Network and resolution enhancement technique.
Algorithm for simplifying the SHA-256 operations tree

- The article offers an algorithm for simplification of Boolean operations tree, which is obtained as a result of analyzing the execution of the SHA-256 hash function. The main idea of the algorithm is that we can reduce the size of the tree by replacing non-optimal Boolean subexpressions with smaller ones. This algorithm can later be used for cryptanalysis of the JSON Web Token protocol as an auxiliary tool that can simplify the search for the key. As a result, we will show that such operations trees can be significantly reduced with offered algorithm.
Similar Questions Identification on Indonesian Language Subject Using Machine Learning

Question similarity is carried out to evaluate similarities between questions in a collection of questions in the question and answer forum and on other platforms. This is done to improve the performance of the question-and-answer forum so that new questions submitted by users can be identified as similar to existing questions in the database. Currently, research related to question similarity is still being carried out on foreign language datasets. The purpose of this research is to identify the similarity of questions in a collection of questions in Indonesian. The method used is Support Vector Machine and IndoBERT. For feature extraction, we evaluate the lexical features and syntax features of each question. For lexical feature extraction, we use the cosine similarity algorithm to calculate the distance between two objects which are represented as vectors. For syntax feature extraction we use the Indonesian part of speech tagger (POS Tag). The dataset used is a collection of questions on Indonesian subjects at the primary and secondary school levels. The results of this study show that the best performance of the Support Vector Machine is obtained from the use of the cosine similarity feature with an accuracy of 85%. While the use of the POS Tag feature or the combination of POS Tag and cosine similarity causes the model to be overfitted and the accuracy decreases to 77%. Meanwhile, for the IndoBERT model, an accuracy of 95% was obtained.
Preimage Awareness in Linicrypt

We extend the analysis of collision-resistant hash functions in the Linicrypt model presented by McQuoid, Swope & Rosulek (TCC 2019) in order to characterize preimage awareness, a security property defined by Dodis, Ristenpart & Shrimpton (Eurocrypt 2009), who also demonstrate its utility in the construction of indifferentiable hash functions. We present a simple and efficiently-checkable property of Linicrypt programs which characterizes preimage awareness. Finally, we show that this characterization may be efficiently automated and as an example, use it to enumerate all preimage-aware compression functions which use two calls to the random oracle. This includes several functions shown to be preimage aware by Dodis et. al. using hand-crafted proofs.
TENTH-GRADE STUDENTS’ READING HABIT

The present study tries to find out how students’ reading behavior in this digital era, especially during Covid 19 pandemic by assessing SMA students’ preference for online materials, their preferred language when reading online materials, the online reading habits of the students, and the amount of time they spend in reading online every day. The study was a survey that involved 44 tenth graders at SMA Negeri 1 Tabukan Utara in Likungan, Sangihe. The data were collected using a questionnaire adapted from Abidin, Pour-Mohammadi, &amp; Jesmin (2011). The data collected were analyzed in percentages. Results of the analysis indicated that (1) the most common activities they are interested in are chatting, surfing the internet, and games, (2) concerning online reading materials, they prefer reading e-books, e-mail, stories, or novels, and comic strips/jokes. The majority of the respondents 89% prefer reading online reading materials in Indonesia, and (3) male respondents read more news, e-mails, and comic strips/jokes. Female respondents, on the other hand, preferred reading e-magazines, e-books, and movie reviews, and (4) the highest percentage of respondents (43%) spent 1-3 hours online daily reading. It is concluded that female respondents spent more time on online reading compared to their male counterparts.
IMPROVING STUDENTS’ VOCABULARY BY USING ANIMATION VIDEO AT THE SEVENTH GRADE OF SMP GPdI BEREA TONDANO

The goal of this research is to determine whether or not using animation videos can improve students' vocabularies. The students of SMP GPdI BEREA Tondano, specifically the 20 students in seventh grade, were selected as the study's subject. One group had pre- and post-tests as part of the pre-experimental design used in this study. Two meetings were needed to complete the study. The research first provides a pre-test, the treatment, and a post-test. The method of data collection uses a multiple-choice test with 20 options as the instrument. This study's findings demonstrate that animation videos can increase students' vocabulary, as evidenced by the higher post-test scores than pre-test scores. The mean pre-test score is 31.5 and the mean post-test score is 80. It is true that watching animated videos can improve kids' vocabularies.
STUDENTS’ PERCEPTION TOWARDS TEACHER’S STRATEGY AT THE COVID-19 PANDEMIC

The purpose of this study is to find out the students' perception toward teachers’ role in motivating students to learn English during the Covid-19 pandemic. In this research, researchers use the qualitative method. To collect the data, the researchers used a questionnaire that consists of 10 questions. The population of this study is the students at SMP Negeri 2 Tondano and the samples are the 8th grade students with a total of 23 students. The samples chosen are the active students who follow the teaching and learning process in their school in online and offline ways. The findings show that students are having great experiences in teaching and learning English. In general, the students respond to the statements very well with positive responses dominated by Agree and Strongly Agree. It showed that the students’ perceptions of the teacher’s role in English learning are various in the Covid-19 pandemic situation. In general, Covid-19 had a significant impact on the world of education, including in the educational system in Indonesia. Traditional and routine learning that emphasizes the interaction of teachers and students in the classroom and outside the classroom shifts to distance learning.
Knowledge distillation based on multi-layer fusion features

Knowledge distillation improves the performance of a small student network by promoting it to learn the knowledge from a pre-trained high-performance but bulky teacher network. Generally, most of the current knowledge distillation methods extract relatively simple features from the middle or bottom layer of teacher network for knowledge transmission. However, the above methods ignore the fusion of features, and the fused features contain richer information. We believe that the richer and better information contained in the knowledge delivered by teachers to students, the easier it is for students to perform better. In this paper, we propose a new method called Multi-feature Fusion Knowledge Distillation (MFKD) to extract and utilize the expressive fusion features of teacher network. Specifically, we extract feature maps from different positions of the network, i.e., the middle layer, the bottom layer, and even the front layer of the network. To properly utilize these features, this method designs a multi-feature fusion scheme to integrate them together. Compared to features extracted from single location of teacher network, the final fusion feature map contains meaningful information. Extensive experiments on image classification tasks demonstrate that the student network trained by our MFKD can learn from the fusion features, leading to superior performance. The results show that MFKD can improve the Top-1 accuracy of ResNet20 and VGG8 by 1.82% and 3.35% respectively on the CIFAR-100 dataset, which is better than state-of-the-art many existing methods.