Reviewerly/exHarmony huggingface dataset:
Accuracy Improvement of Personal Recognition based on EMG

In this research, we aim to design a highly accurate personal recognition system using myoelectric potential. A general method of personal recognition based on EMG utilizes muscle potential during certain gestures. The key idea of the general method of personal recognition based on EMG signals is to construct a discriminator that can identify the gesture from the EMG signals generated when a certain gesture is performed. The principle of the discriminator is that when a series of gestures is performed, the discriminator identifies the correct gesture if the EMG signals are generated by the true user, but does not from the EMG signals generated by other users, even if they perform the same gesture. In previous studies, there has been a problem of high misidentification rate, i.e., the system does not recognize the true user or misidentifies other users as the true user. Therefore, this study strives to improve the accuracy of personal regcognition systems based on EMG by using deeplearning to find personal features, and based on features to construct a discriminator that can identify the gesture from the EMG signals generated.
Analisis Sentimen Ulasan E-Commerce Pakaian Berdasarkan Kategori dengan Algoritma Convolutional Neural Network

Almost everyone looks at reviews before deciding to buy an item in e-commerce. Consumers say that online reviews influence their purchasing decisions. Based on these data, consumers need sentiment reviews to make a decision to choose a product/service. However, the results of the sentiment analysis are still less specific, so the review classification process is carried out based on the review category. Sentiment classification process based on clothing category is carried out using the Convolutional neural network method. The amount of data used is 3384 data with 3 categories. The category classification model shows good performance. When evaluated with testing data (unseen data), the accuracy value is 88%, the precision value is 88%, recall is 88% and the f1-score is 88%. For the sentiment classification model with the bottoms category, the resulting accuracy value is 80%, precision is 81%, recall is 80%, and f1-score is 79%. For the sentiment classification model with the dresses category, the accuracy value is 81%, precision is 81%, recall is 81%, and f1-score is 81%. For sentiment classification with the tops category the resulting accuracy value is 77%, precision is 77%, recall is 77%, and f1-score is 77%.
Model Compression Based Lightweight Online Signature Verification Framework

Advances in networking and digital technologies have led to the widespread usage of Online Signature Verification (OSV) frameworks in real-time settings to validate a user's identity. Because of the superior performance of Deep Learning frameworks, CNN-based models have been widely used for solving difficult computer vision tasks such as Object Detection, Object Segmentation, and so on. The biggest impediment to OSV adoption of CNN-based models is the growing size of CNN models. This prohibits OSV frameworks from being widely used in devices with minimal computational resources, such as mobile/ embedded devices. The newly popular topic of CNN model pruning aims to solve this problem by deleting filters and neurons that do not contribute to the model's learning. Optimal CNN-based OSV frameworks are obtained by removing the less important filters and neurons and fine-tuning the pruned networks. In line with this, we offer a novel light weight OSV architecture in which a detailed ablation research is performed to examine the contribution of each layer, and non-contributive layers are deleted based on the analysis. As a result, ideal low weight models with improved classification accuracies and the ability to be applied in real-time devices emerge. Our model's performance is thoroughly tested on three commonly used datasets: MCYT-100 (DB1), SVC, and SUSIG. In MCYT-100, SVC, and SUSIG datasets, the pruned model achieves a state-of-the-art EER of 7.98%, 3.65%, and 12.39% in the skilled-1 group, respectively. The efficiency of pruning-based OSV frameworks has been demonstrated in experiments.
Effects of Feature Selection with Machine Learning Algorithms in Detection of Credit Card Fraud

As an effect of developments in e-commerce systems and communication technologies, Credit cards have become the most common mode of payment for purchases. The payments through the credit cards also involve the risk of credit card fraud such as application fraud, identity theft, lost/stolen card misuse, and phishing. These frauds lead to huge losses and require automatic and real-time fraud detection. Many studies have used Machine Learning (ML) techniques to detect fraudulent transactions. This study focuses on proposing a framework for the detection of credit card frauds by applying machine learning techniques like Random Forest (RF) and Naïve Bayes and testing the results on balanced and unbalanced datasets with and without performing the feature selection on the dataset. After comparing the results, it was discovered that Random Forest outperformed the Naïve Bayes on a balanced dataset with feature selection performed using Recursive Feature Elimination and Information Gain.
Enkripsi Gambar Berdasarkan Modifikasi Bit Piksel Dengan Menggunakan Perpaduan Logistic Map Dan Henon Map

Data theft using malware attacks in this digital era can attack smartphones and cloud servers, in 2018 there was the theft of photos belonging to Aryono Huboyo Djati. Theft can be prevented by applying cryptographic techniques. The purpose of this study was to determine the ability to combine Logistic Map and Henon Map in image encryption and decryption. These two theories were chosen because they are sensitive, with a small change in the input value will have a very significant impact on the output value. First step is changing the image into a matrix and randomizing using the Logistic Map algorithm and then continuing with the XOR process for each pixel bit using the Henon Map algorithm. in this study the encrypted image obtained an average MSE value exceeding 400 and the average PSNR value less than 10dB, this indicates that the cipher image is different from the plain image, and the decryption results show the average MSE value is 0 and the average PSNR value average is 100, this indicates that there is no difference between the description image and the original image
Using Google Docs as A Collaborative Writing Medium For Empowering Senior High School Students' Writing Skills

This study investigates the effect of using Google Docs online learning media in conjunction with collaborative writing strategies on high school students' writing skills. The study took place at Surabaya's high schools, with 16 tenth-grade students participating. The research is a pre-experimental design with a one-group pretest and posttest design. The study's design involved only one class in the treatment of Google Docs media in collaborative writing. The first research question focused on implementing Google Docs and collaborative writing activities in the learning process conducted by a teacher and students. The findings were determined from observational sheets. Further research findings for the second research question, which were derived from students’ pre-test and post-test scores, were examined using t-tests and presented quantitatively. Additionally, questionnaires were used to determine students' perceptions of the use of Google Docs as a collaborative medium in their writing studies. According to research findings, students' scores improved significantly following treatment compared to their pre-treatment scores. Cohen's d was used to determine the magnitude of the effects created by this study, and it was discovered that the effects were quite significant. The results for the second research question indicated that the learning process was done successfully. Students' attitudes towards the treatment also showed that most groups performed well during the activity. Furthermore, the third research question results indicated that the combination of Google Docs and collaborative writing methods elicited favorable responses from students. Thus, collaborative writing strategies utilizing Google Docs as a medium can assist Senior High School students in writing more effectively.
Securing Management Information Systems Using Blockchain Technology

This study aims to present the basic principles of blockchain technology that has received the attention of various sectors, including the higher education sector, which studies the application of these technologies to improve information traceability, accountability, and integrity, while ensuring privacy, transparency, durability, trustworthiness, and authenticity. Various interesting proposals and projects launched and being developed, including verification of digital certificates. Through this study, we are building a digital certificate validation system that overcomes the limitations of paper-based digital certificates and non-blockchain-based digital certificates. It explains how to verify a certificate and gives a new idea to create a certificate in the most secure and tamper-resistant way using blockchain technology.
Integrativni pristup sadržajima Digitalnog sveta i Sveta oko nas

The contemporary approach to teaching aims to help children develop a complete picture of the world from an early age, to which the subjects The World Around Us and The Digital World also contribute by holistically viewing natural, social, and digital phenomena. The aim of this paper is to investigate whether, and to what extent, common content that can be integratively related is represented in the curricula and textbooks for the first and second grade of The Digital World and The World Around Us. The curriculum content analysis included the common content identification for achieving cross-curricular integration, while the textbook analysis for The Digital World (N = 8) involved determining the number and type of integrative examples within the topic Algorithmic thinking. The identified related content suggests the need for integrating these two subjects so as to better combine knowledge into a meaningful whole, though some disagreements with regard to the timing of the introduction of certain concepts were observed in the curricula.
A Multi-Label Classification Method for Anomaly Detection of Bridge Structural Health Monitoring Data

&lt;p&gt;In past years, massive data has been accumulated by many bridge structural health monitoring systems, and various methods have been proposed to detect data anomalies to ensure the reliability of subsequent data analysis. However, these methods are incapable of determining if there still exist usable data segments in a data sequence providing a specified anomaly type has been identified. To address the problem, a deep learning-based multi-label classification method is proposed in this paper. A multi-label anomaly dataset is first constructed using monitored acceleration data of a cable-stayed bridge. Then, a multilabel anomaly classification model based on a convolutional neural network is developed and trained with the constructed dataset. The developed method exhibits desirable performance in simultaneously detecting the existence of both usable data and the other data anomalies.&lt;/p&gt;
Mobilna aplikacija u funkciji unapređenja predškolskog vaspitanja i obrazovanja

The covid-19 pandemic has accelerated the implementation of distance learning and digital technologies in education at all levels of the education system, including preschool education. This paper first analyzes the research conducted in the preschool institution on its readiness for work in times of crisis, when there is no direct interaction between children and educators. The analysis found that the education in the preschool institution before the pandemic relied on offline educational techniques and tools, whereas during the pandemic, the Internet, web services, mobile applications, and distance learning became the mainstay of education. The second part of the research refers to the representation and structure of educational mobile applications in Serbian for preschool children. Based on both these studies, the paper proposes a model for the use of mobile applications to improve preschool education in difficult conditions such as those that emerged as a result of the Covid-19 pandemic.
Sparse Kernel Transfer Learning

Deep learning performs remarkably well in a supervised learning setting. For computer vision tasks, the standard architecture is a convolutional neural network containing several layers of convolution and pooling, followed by a fully connected layer with a softmax output layer. Due to advancements in algorithms and activations, random initialization of the weights of the network can be effectively trained through stochastic gradient descent. In the event that you have a smaller dataset, one can utilize a transfer learning technique of the network weights in order to achieve state-of-the-art accuracy in over-parameterized models. We demonstrate that a transfer learning of a different kind yields better results over a randomized initialization of the network, and even over the standard transfer learning method in specific situations. In this paper, we describe sparse kernel transfer learning, a method that utilizes sparse coding and dictionary learning to pre-train the filters of a CNN. This pre-training is reminiscent of the unsupervised autoencoder training that used to be performed when stacking layers of a neural network. We argue that this dictionary transfer provides a better initialization, results in better classification, is more interpretable, and can even outperform the state-of-the-art models in transfer learning in certain datasets.
Seamless Gaze Gesture Interactions With Various Devices Without Moving Objects as Visual Stimulation

Although the current pandemic is expected to eventually end, similar outbreaks of viral disease may occur in the future. Such outbreaks can spread through direct social contact. To avoid such risks, we propose contactless interaction using an eye-tracking device. The proposed approach uses a conventional visible-light camera to detect a user’s iris. The camera is attached to a wearable frame. We believe that our proposed method is safer than exposing a user’s eyes to an infrared sensor at a close distance. Furthermore, we considered seamless multi-device control using the proposed device. The system utilizes QR codes to switch between controlled devices. Once the eye-tracking device detects a QR code, it connects to a target device and sends commands to control it. These commands are triggered by eye movement gestures performed by the user. We defined these control gestures as a simple straight eye movement in a given direction, to avoid the potential burden of requiring complex gestures. We experimentally implemented the proposed device, and the results show that it achieved an overall accuracy of 81.66%. Thus, it may prove useful for multitasking applications or for contactless interaction.
Introduction

After a collective of over 50 years working as software engineers, we want to have an honest conversation with you, the reader of this book, on why agile software projects and teams fail. Over the course of our careers, we navigated a variety of consulting companies, large corporations such as IBM, mid-sized, aggressive industry leaders such as Shutterstock and AlphaSights, and small, fast-paced startups.
A Proof of Concept Implementation of Explainable Artificial Intelligence (XAI) in Digital Forensics

Explainable Artificial Intelligence (XAI) has been a subject of much research related to its potential to have a transformative impact on industries where AI implementations require a high degree of trust. Digital Forensics is one such field, with practitioners dealing with an ever-increasing volume of data and a variety of devices. This research first examines the concept of XAI and what it could accomplish in the Digital Forensics field. Using of series of 23 manufactured virtual hard disk (VHD) images, AI models were trained to make predictions on image and video file content and file system metadata. The manufactured VHD images can be of independent interest for educational purposes as well. The predictions were then processed using the Local Interpretable Model-Agnostic Explanations (LIME) toolkit. These results were then used as the basis for a discussion on the future of XAI in the digital forensics industry and potential areas for further research.
On the Optimal Communication Complexity of Error-Correcting Multi-server PIR

An $$\ell $$ -server Private Information Retrieval (PIR) scheme enables a client to retrieve a data item from a database replicated among $$\ell $$ servers while hiding the identity of the item. It is called b-error-correcting if a client can correctly compute the data item even in the presence of b malicious servers. It is known that b-error correction is possible if and only if $$\ell >2b$$ . In this paper, we first prove that if error correction is perfect, i.e., the client always corrects errors, the minimum communication cost of b-error-correcting $$\ell $$ -server PIR is asymptotically equal to that of regular $$(\ell -2b)$$ -server PIR as a function of the database size n. Secondly, we formalize a relaxed notion of statistical b-error-correcting PIR, which allows non-zero failure probability. We show that as a function of n, the minimum communication cost of statistical b-error-correcting $$\ell $$ -server PIR is asymptotically equal to that of regular $$(\ell -b)$$ -server one, which is at most that of $$(\ell -2b)$$ -server one. Our main technical contribution is a generic construction of statistical b-error-correcting $$\ell $$ -server PIR for any $$\ell >2b$$ from regular $$(\ell -b)$$ -server PIR. We can therefore reduce the problem of determining the optimal communication complexity of error-correcting PIR to determining that of regular PIR. In particular, our construction instantiated with the state-of-the-art PIR schemes and the previous lower bound for single-server PIR result in a separation in terms of communication cost between perfect and statistical error correction for any $$\ell >2b$$ .
Manipulation Planning of Wiring a Cable with Connector Considering Shape Transition of Deformable Linear Object

In this paper, we propose a method of manipulation planning for cable wiring. The method enables to take into account the deformation of cables while connector incorporation process. Using a simulation that predicts the shape of a cable based on the posture of both ends of the cable, we generate a connector manipulation path that avoids contact between the cable and obstacles. We conducted simulation and confirmed that the proposed method can be used for inserting a connector into a socket installed on a circuit board.
FRAUD DETECTION IN AUTOMOTIVE INSURANCE DOMAIN USING MACHINE LEARNING

The insurance industry is one of the most vital aspects of modern society, the economy, and people's lives. It brings peace and security to people by offsetting the financial risks of damage and loss. Fraud in the Automotive Insurance domain
Cross-Layer Feature Attention Module for Multi-scale Object Detection

Recent target detection networks adopt the attention mechanism for better feature abstraction. However, most of them draw feature attentions from merely one or two layers, failing to obtain consistent results for objects with different scales. In this paper, we propose a cross-layer feature attention module (CFAM) which can be plugged in any off-the-shelf architecture, and demonstrate that attentions obtained from multiple layers can further improve object detection. The proposed module consists of two components for cross-layer feature fusion and feature refinement, respectively. The former collects rich contextual cues by fusing the features from distinct layers, while the later calculates the cross-layer attention maps and applies them with the fused features. Experiments show the proposed module improves the detection rate by 2% against the baseline architecture, and outperforms recent state-of-the-art methods on the Pascal VOC benchmark.
Transform Domain End-to-end Learning Communication System

Unknown time-varying interference is a common and practical problem for communication system. An end-to-end learning communication system with transform domain interference suppression is proposed in this paper to suppress the real-time interference for the autoencoder(AE) neural networks. The basic idea is to dynamical shape the waveform in transform domain at both the AE transmitter and the receiver to avoid interfered spectral regions. The simulation shows that the proposed method can save 2dB Eb/N0 at bit error ratio 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−4</sup> than AE-based communication system with 20% wideband time-varying interference.
1726 Integrating established clinical scores with a novel transcriptomic severity classifier augments early risk assessment in the ED

Aims, Objectives and Background Reliable risk assessment in patients presenting to emergency departments (ED) with suspected infection is of utmost importance to support clinical decisions. Vital sign-based scoring systems such as NEWS2 or qSOFA enable a rapid first assessment of patient urgency at triage. However, their inherent high sensitivity might drive over-utilization of healthcare resources. Our aim was to evaluate if adding the result of a transcriptomic severity classifier can synergistically improve current score-based risk assessment in the ED. Method and Design We performed a secondary analysis of a patient cohort (n=312) enrolled in the Charité University hospital ED (Berlin, Germany) with suspected infection and at least one vital sign alteration. The expression of 29-host mRNAs in PAXgene-stabilized whole blood was quantified using NanoString nCounter® SPRINT. The proprietary machine learning classifier IMX-SEV-3 was applied to calculate a score that falls into pre-defined interpretation bands: low/moderate/high severity. NEWS2 and qSOFA were documented on admission and combined with the classifier results to analyze the incidence of two clinical endpoints: ‘need for critical care’ (composite of need for ventilation, dialysis, and/or vasopressors) within 7d and ‘28d mortality’. Results and Conclusion Among enrolled patients, 22 (7.1%) died and 66 (21.1%) required ICU-level care. Of patients with a high NEWS2 (≥5 points; n=184), there was a stepwise increase in mortality among the low (0%; n=0/47), medium (10.1%; n=12/119) and high (44.4% n=8/18) IMX-SEV-3 severity subgroups. A similar stratification was achieved across the low (17%), moderate (31%), and high (61%) IMX-SEV-3 subgroups for prediction of critical care. More granular risk stratification could also be confirmed when using IMX-SEV-3 in combination with high qSOFA (≥2 points; n=76): 0/10.6/50% mortality and 23.5/40.4/66% need for critical care in the low/moderate/high subgroups, respectively. In summary, the combined use of immune-based IMX-SEV-3 results for ED patients with high clinical scores allows improved prediction of mortality and the need for critical care.