Reviewerly/exHarmony huggingface dataset:
Formation of students' competitiveness in the vuca world

The purpose of the article is to study the influence of the Vuca-world on the professional training of students and their competitiveness. The study presents an analysis of adaptations of an agile approach that meets the requirements of Vuca, a test of the formation of the level of competitiveness of students of a higher education institution according to the selected criteria. The study found that the training of students in the Vuca-world is becoming more technological, aimed at the formation of independent and creative specialists who can solve problems in interaction. The level of students' competitiveness in the new environment is getting higher.
Accident detection and road monitoring in real time using deep learning and lane detection algorithms

Automating the monitoring of the roads would mean safer roads for both car drivers and pedestrians. The objectives of the system were to build a real time surveillance system for intelligent roads of the future. The system should be able to detect lane lines, cars, pedestrians and other moving vehicles on the road. In case a car goes off its course, the system should report it as an accident. Relying on machine learning technology, a real time detection based neural network was constructed to be trained on picking up objects in any given scene like cars, buses, pedestrians, bikes and motor bikes. Also, an algorithm was designed to detect lane lines and highlight them on the screen using image processing techniques and edge detection. The system achieved its main required target which is being able to detect objects in almost real time.
Detecting Uncertainty of Mortality Prediction Using Confident Learning

Early mortality prediction is an actively researched problem that has led to the development of various severity scores and machine learning (ML) models for accurate and reliable detection of mortality in severely ill patients staying in intensive care units (ICUs). However, the uncertainty of such predictions due to irregular patient sampling, missing information, or high diversity of patient data has not yet been adequately addressed. In this paper, we used confident learning (CL) to incorporate sample-uncertainty information into our mortality prediction models and evaluated the performance of these models using a large dataset of 139,367 unique ICU admissions within the eICU Collaborative Research Database (eICU-CRD). The results of our study validate the importance of uncertainty quantification in patient outcome prediction and show that the state-of-the-art ML models augmented with CL are more robust against epistemic error and class imbalance.
Predicting and Classifying Breast Cancer Using Machine Learning

The proposed research work aims to develop a method to predict and classify breast cancer (BC) at an early stage. In this research, three models are developed, and their performance is compared against each other. The first model was built using one of the machine learning algorithms called support vector machine (SVM), the second model was built using a deep learning algorithm called convolutional neural networks (CNNs), and the third model combines CNNs with a transfer learning technique for delivering better results. The data set is provided by the BC Histopathological Image Classification (BreakHis). All models are trained on the training set with two main categories: benign tumor and malignant tumor. The malignant tumor category is divided into subsets of invasive carcinoma tumors and in situ carcinoma tumors. Furthermore, invasive carcinoma tumors are classified into grade 1, grade 2, or grade 3, where grade 3 is the highest and is more aggressive. The results show that the accuracies of biopsy image classification using SVM are 92%, the accuracy of CNN is 94%, and the accuracy of CNN using the transfer learning technique is 97%. The results of this research will be beneficial in the early diagnosis of BC and help doctors in making better decisions and medical interventions.
On Designing, Composing and Performing Networked Collective Interactions

In this article, we discuss some of our research with Local Area Networks (LAN) in the context of sound installations or musical performances. Our systems, built on top of Web technologies, enable novel possibilities of collective and collaborative interaction, in particular by simplifying public access to the artwork by presenting the work through the web browser of their smartphone/tablet. Additionally, such a technical framework can be extended with so-called nano-computers, microprocessors and sensors. The infrastructure is completely agnostic as to how many clients are attached, or how they connect, which means that if the work is available in a public space, groups of friends, or even informally organised flash mobs, may engage with the work and perform the contents of the work at any time, and if available over the Internet, at any place. More than the technical details, the specific artistic directions or the supposed autonomy of the agents of our systems, this article focuses on how such ‘networks of devices’ interleave with the ‘network of humans’ composed of the people visiting the installation or participating in the concert. Indeed, we postulate that an important point in understanding and describing such proposals is to consider the relation between these two networks , the way they co-exist and entangle themselves through perception and action. To exemplify these ideas, we present a number of case studies, sound installations and concert works, very different in scope and artistic goal, and examine how this interaction is materialised from several standpoints.
Linguistic Parameters of Spontaneous Speech for Identifying Mild Cognitive Impairment and Alzheimer Disease

Abstract In this article, we seek to automatically identify Hungarian patients suffering from mild cognitive impairment (MCI) or mild Alzheimer disease (mAD) based on their speech transcripts, focusing only on linguistic features. In addition to the features examined in our earlier study, we introduce syntactic, semantic, and pragmatic features of spontaneous speech that might affect the detection of dementia. In order to ascertain the most useful features for distinguishing healthy controls, MCI patients, and mAD patients, we carry out a statistical analysis of the data and investigate the significance level of the extracted features among various speaker group pairs and for various speaking tasks. In the second part of the article, we use this rich feature set as a basis for an effective discrimination among the three speaker groups. In our machine learning experiments, we analyze the efficacy of each feature group separately. Our model that uses all the features achieves competitive scores, either with or without demographic information (3-class accuracy values: 68%–70%, 2-class accuracy values: 77.3%–80%). We also analyze how different data recording scenarios affect linguistic features and how they can be productively used when distinguishing MCI patients from healthy controls.
Dynamic Random Probing Expansion with Quasi Linear Asymptotic Complexity

The masking countermeasure is widely used to protect cryptographic implementations against side-channel attacks. While many masking schemes are shown to be secure in the widely deployed probing model, the latter raised a number of concerns regarding its relevance in practice. Offering the adversary the knowledge of a fixed number of intermediate variables, it does not capture the so-called horizontal attacks which exploit the repeated manipulation of sensitive variables. Therefore, recent works have focused on the random probing model in which each computed variable leaks with some given probability p. This model benefits from fitting better the reality of the embedded devices. In particular, Belaïd, Coron, Prouff, Rivain, and Taleb (CRYPTO 2020) introduced a framework to generate random probing circuits. Their compiler somehow extends base gadgets as soon as they satisfy a notion called random probing expandability (RPE). A subsequent work from Belaïd, Rivain, and Taleb (EUROCRYPT 2021) went a step forward with tighter properties and improved complexities. In particular, their construction reaches a complexity of \(\mathcal {O}(\kappa ^{3.9})\), for a \(\kappa \)-bit security, while tolerating a leakage probability of \(p=2^{-7.5}\).In this paper, we generalize the random probing expansion approach by considering a dynamic choice of the base gadgets at each step in the expansion. This approach makes it possible to use gadgets with high number of shares –which enjoy better asymptotic complexity in the expansion framework– while still tolerating the best leakage rate usually obtained for small gadgets. We investigate strategies for the choice of the sequence of compilers and show that it can reduce the complexity of an AES implementation by a factor 10. We also significantly improve the asymptotic complexity of the expanding compiler by exhibiting new asymptotic gadget constructions. Specifically, we introduce RPE gadgets for linear operations featuring a quasi-linear complexity as well as an RPE multiplication gadget with linear number of multiplications. These new gadgets drop the complexity of the expanding compiler from quadratic to quasi-linear.
Localization performance of hum-noise-based touch sensor (HumTouch) with unknown participants

HumTouch is a touch sensing method that can turn object surfaces into touch sensors. The sensing method utilizes the current leak from the human body; therefore, the localization performance exhibits individual differences. In this paper, we research the localization performance of HumTouch involving multiple participants. A semi-conductive paper marked with 49 points was used for the experiment. Five participants participated in the experiments and the localization results were evaluated via leave-one-out cross validation, in which an estimation model was established by using the samples collected from four participants to localize the touch of the last participant. For four out of the five participants, there were mean estimation errors smaller than or equal to 1 cm, which is comparable to the size of a fingertip. This indicates that HumTouch has the potential to be applied to multiple participants without prior calibration conducted for a specific person.
Coded Websites vs Wordpress Websites

This document gives multiple instructions related to web developers using older as well as newer technology. Websites are being created using newer technologies like wordpress whereas on the other hand many people prefer making websites using the traditional way. This document will clear the doubt whether an individual should use wordpress websites or coded websites according to the users convenience. The Responsiveness of the websites, the use of CMS nowadays, more and more up gradation of technologies with SEO, themes, templates, etc. make things like web development much much easier. The aesthetics, the culture, the expressions, the features all together add up in order make the designing and development a lot more efficient and effective. Digital Marketing has a tremendous growth over the last two years and yet shows no signs of stopping, is closely related with the web development environment. Nowadays all businesses are going online due to which the impact of web development has become such that it has become an integral part of any online business.
Leveraging Human Visual Perception for an Optimized Virtual Reality Experience

We explore perceptually motivated research through virtual reality (VR) with the human as an experimental medium. We track, study, and model our spatio-temporal gaze behaviors to understand cognitive responses. Furthermore, we leverage this knowledge to advance computer graphics techniques in VR with more efficient rendering, increased display comfort, and safer locomotion.
Blockchain Enabled Intelligence of Federated Systems (BELIEFS): An attack-tolerant trustable distributed intelligence paradigm

In this article, a Blockchain Enabled Intelligence of Federated Systems (BELIEFS) is proposed to conduct cooperative control for the multi-regional large-scale power system with a multi-agents system (MAS). By establishing a two levels blockchain, each regional AI agent can simultaneously manage intra-regional controllers and cooperate with other AI agents. Under the consensus mechanism, the agents, which respectively conducted distributed deep reinforcement learning (DDRL) algorithm in multi-regions, can have the tolerant capability of malicious attacks in their training process. The demonstration of the proposed approach is within a multi-regional large-scale interconnected power system. Under the mode of “centralized dispatching and hierarchical management”, this article aims to definite a mathematical model to deal with the control problem of the power systems. With the comparison experiments, the effectiveness and efficiency of our proposed method in the training process are verified. In addition, malicious attacks are set on the main chain and shard chains to verify the attack-tolerant capability. We expect that such approach and results can suggest a new paradigm of attack-tolerant trustable distributed AI deployment.
Towards Full-to-Empty Room Generation with Structure-aware Feature Encoding and Soft Semantic Region-adaptive Normalization

The task of transforming a furnished room image into a background-only is extremely challenging since it requires making large changes regarding the scene context while still preserving the overall layout and style. In order to acquire photo-realistic and structural consistent background, existing deep learning methods either employ image inpainting approaches or incorporate the learning of the scene layout as an individual task and leverage it later in a not fully differentiable semantic region-adaptive normalization module. To tackle these drawbacks, we treat scene layout generation as a feature linear transformation problem and propose a simple yet effective adjusted fully differentiable soft semantic region-adaptive normalization module (softSEAN) block. We showcase the applicability in diminished reality and depth estimation tasks, where our approach besides the advantages of mitigating training complexity and non-differentiability issues, surpasses the compared methods both quantitatively and qualitatively. Our softSEAN block can be used as a drop-in module for existing discriminative and generative models. Implementation is available on vcl3d.github.io/PanoDR/.
Team Hitachi @ AutoMin 2021: Reference-free Automatic Minuting Pipeline with Argument Structure Construction over Topic-based Summarization

This paper introduces the proposed automatic minuting system of the Hitachi team for the First Shared Task on Automatic Minuting (AutoMin-2021). We utilize a reference-free approach (i.e., without using training minutes) for automatic minuting (Task A), which first splits a transcript into blocks on the basis of topics and subsequently summarizes those blocks with a pre-trained BART model fine-tuned on a summarization corpus of chat dialogue. In addition, we apply a technique of argument mining to the generated minutes, reorganizing them in a well-structured and coherent way. We utilize multiple relevance scores to determine whether or not a minute is derived from the same meeting when either a transcript or another minute is given (Task B and C). On top of those scores, we train a conventional machine learning model to bind them and to make final decisions. Consequently, our approach for Task A achieve the best adequacy score among all submissions and close performance to the best system in terms of grammatical correctness and fluency. For Task B and C, the proposed model successfully outperformed a majority vote baseline.
Visual Perception for Smart City Defense Administration and Intelligent Premonition Framework Based on DNN

A detailed methodology of object detection in a smart city setting has been illustrated in this chapter. The presented methodology focuses on intelligent uses of machine learning and deep learning algorithms for the effective extraction of the desired ROI from a challenging backdrop. This chapter encompasses a Convolutional Neural Network (CNN)-based architecture and a Faster RCNN-based approach and holistic comparisons are being made between the results obtained from the different approaches. The problem that has been tried to address through this work is the lack of robust algorithms that can detect occluded objects effectively, which may commonly occur in a smart city. So, the primary focus of this work is to devise a methodology that can detect even minute objects camouflaged in a city crowd, which are prevalent in smart cities across India. This work is believed to be beneficial in various sectors, even in the military, to help them with reconnaissance tasks. Further, the proposed methodology is equipped with an alarm system that warns against plausible security breaches and intrusion. This mechanism enhances the security of a smart city using IoT techniques. The entire methodology described in the chapter can be deployed without the use of any additional hardware. Overall, the proposed framework is robust, effective and viable for multi-facet uses in the future and can be effectively deployed in large distributed systems across smart cities of India.
Distributed AI embedded cluster for real-time video analysis systems with edge computing

Herein, on the basis of a distributed AI cluster, a real-time video analysis system is proposed for edge computing. With ARM cluster server as the hardware platform, a distributed software platform is constructed. The system is characterized by flexible expansion, flexible deployment, data security, and network bandwidth efficiency, which makes it suited to edge computing scenarios. According to the measurement data, the system is effective in increasing the speed of AI calculation by over 20 times in comparison with the embedded single board and achieving the calculation effect that matches GPU. Therefore, it is considered suited to the application in heavy computing power such as real-time AI computing.
Deteksi Kesamaan Teks Jawaban pada Sistem Test Essay Online dengan Pendekatan Neural Network

E-learning is an online learning system that applies information technology in the teaching process. E-learning used to facilitate information delivery, learning materials and online test or assignments. The online test in evaluating students’ abilities can be multiple choice or essay. Online test with essay answers is considered the most appropriate method for assessing the results of complex learning activities. However, there are some challenges in evaluating students essay answers. One of the challenges is how to make sure the answers given by students are not the same as other students answers or 'copy-paste'. This study makes a similarity detection system (Similarity Checking) for students' essay answers that are automatically embedded in the e-learning system to prevent plagiarism between students. In this paper, we use Artificial Neural Network (ANN), Latent Semantic Index (LSI), and Jaccard methods to calculate the percentage of similarity between students’ essays. The essay text is converted into array that represents the frequency of words that have been preprocessed data. In this study, we evaluate the result with mean absolute percentage error (MAPE) approach, where the Jaccard method is the actual value. The experimental results show that the ANN method in detecting text similarity has closer performance to the Jaccard method than the LSI method and this shows that the ANN method has the potential to be developed in further research.
Developing and evaluating cybersecurity competencies for students in computing programs

Changes due to technological development in the workplace are putting pressure on academia to keep pace with the changing nature of work. Due to the growing need for cybersecurity professionals, universities improve their cybersecurity programs to develop qualified cybersecurity competencies. The purpose of this study is to validate the cybersecurity knowledge, skills, and abilities (KSAs) competencies of cybersecurity degree programs using a fuzzy linguistic group decision-making method. This study shows that cybersecurity knowledge is essential, along with technical skills and human abilities for cybersecurity professionals.
A BERT-based Hate Speech Classifier from Transcribed Online Short-Form Videos

With the rise of human-centric technologies such as social media platforms, the amount of hate also continues to grow proportionally with the increasing number of users worldwide. TikTok is one of the most-used social media platforms due to its feature that allows users to express themselves via creating and sharing short-form videos based on any desired topic and content. In addition, it has also become a platform for political discourse and mudslinging as users can freely express an opinion and indirectly debate with random people online. In this study, we propose the use of BERT, a complex bidirectional transformer-based model, for the task of automatic hate speech detection from speech transcribed from Tagalog TikTok videos. Results of our experiments show that a BERT-based hate speech classifier scores 61% F1. We also extended the task beyond several algorithms such as LSTM, Naïve Bayes, and Decision Tree and found out that traditional methods such as a simple Bernoulli Naïve Bayes approach remain at par with the BERT model.
Students’ Perception of Scrum in a Course Project

Gaining insight into what students experience in a course in software engineering is essential to the continuous development of the course design.The growing use of agile approaches in professional settings has facilitated their introduction into training and undergraduate courses in software engineering.This paper reports a case study of student participation in a course project by using a previously presented training model.We aimed to assess how students felt with the training model when conducting the 5 Scrum events: Sprint Planning, Daily Meeting, Sprint, Sprint Review, and Sprint Retrospective.To achieve our goal, we applied a 22-item survey to 31 undergraduate students to gather students' opinions.Results showed that most students had a positive opinion about team communication, the position of Scrum Master, Sprint goals definition, and guidance using meetings as a means of checkpoints.
Redirected jumping in virtual scenes with alleys

The redirected jumping (RDJ) technique is a new locomotion method that saves physical tracking area and enhances the body movement experience of users in virtual reality. In a previous study, the range of imperceptible manipulation gains in RDJ was discussed in an empty virtual environment (VE). In this study, we conducted three tasks to investigate the influence of alley width on the detection threshold of jump redirection in a VE. The results demonstrated that the imperceptible distance gain range in RDJ was not associated with the width of the alleys. The imperceptible height and rotation gain ranges in RDJ are related to the width of the alleys. We preliminarily summarized the relationship between the occlusion distance and manipulation range of the three gains in a complex environment. Simultaneously, the guiding principle for choosing three gains in RDJ according to the occlusion distance in a complex environment is provided.