Reviewerly/exHarmony huggingface dataset:
Analyzing Gaze Data During Rest Time/Driving Simulator Operation Using Machine Learning

Research on drivers often uses the driving simulator (DS). Visually induced motion sickness (VIMS) has been pointed out as a problem in DS experiments. Although there are many methods to evaluate VIMS, reducing the burden on the experimental collaborators is an issue. As a method of measuring physiological indices that is less burdensome to the participants and has many applications, the use of noncontact eye-tracking system has been mentioned. This study developed a VIMS evaluation index using data collected with a noncontact eye-tracking system for DS experiments. The participants included eight elderly people with visual and balance functions that did not interfere with their daily life. The participants’ gaze data were measured at all DS trials and they answered the simulator sickness questionnaire (SSQ) before and after each trial. The participants were divided into two groups on the basis of their SSQ results. One group experienced VIMS during the DS trial (four people; average age, 79.0 years), whereas the other group did not experience it (four people; average age, 72.0 years). The results of the learning model’s validation showed a high rate of correct answers. The results suggested that the learning model obtained using machine learning was an effective evaluation index for VIMS during the DS trial.
Extreme Learning With Metaheuristic Optimization for Exchange Rate Forecasting

Model with better learning ability and lower structural complexity is desirous for accurate exchange rate forecasting. Faster convergence to optimal solutions has always been a goal for the researcher in building forecasting models. And this is achieved by extreme learning machines (ELMs) due to their single hidden layer architecture and superior generalization ability. ELM is a simple training algorithm used to find the hidden-output layer weights by a random selection of input-hidden layer weights. Metaheuristics algorithms like Fireworks algorithm (FWA), Chemical reaction optimization (CRO), and Teaching learning-based optimization (TLBO) are employed to pre-train the ELM owing to their fewer optimizing parameters. This article aims to pre-train ELM using the said metaheuristics separately, ensuring the optimal solution of a single feedforward network (SLFN) with improved accuracy. The pre-trained ELMs provide accurate results. The same was verified using other primitive optimization algorithms
Image Splicing Detection Using Feature Based Machine Learning Methods and Deep Learning Mechanisms

Digital Image forgery has become widespread nowadays. Fraudsters can intentionally use forged images for different illegitimate and malicious purposes. One of the most severe forms of image forgery is Image Splicing which can be defined as making an artificial image by joining multiple parts of different source images to form a natural-looking forged image. Image Splicing Detection has a fundamental importance in Digital Forensics and Cyber Security. The detection mechanism of Image Splicing is of two types – Feature-based schemes with Machine Learning and methods based on Deep Learning. In this paper, different machine learning-based and deep learning-based research works have been briefly discussed to detect image splicing along with experimental results. Then discussion about the datasets explored, different performance metrics, and a comparative study of the methods is also presented. Finally, it is concluded with a discussion about the possible future scope of research in this regard.
The Influence of Online Learning, the Use of Social Media, Learning Motivation on the Achievements of Students of Citra Negara Vocational High School

This study aims to see the influence of online learning, the use of social media, student learning motivation on student learning achievement. This study used quantitative methods with a research sample of 160 people.The results of this study show that the influence of online learning provides positive results on the learning achievement of students of SMK Citra Negara, the use of social media gives positive results to student learning achievements of SMK Citra Negara, and student motivation gives negative results on the learning achievements of students of SMK Citra Negara.
Influence of the Pandemic in the Upskilling of Teachers’ Digital Competence in Remote Teaching in Basic Education in the Philippines

With the occurrence of the pandemic and the necessity to deliver full scale online distance learning, the use of varied digital tools has been magnified. This study aims to determine the extent of influence of the pandemic in the upskilling of teachers’ digital competence and the use of digital tools in remote teaching in basic education. Perceptions on Technology Integration and Training Proficiency and Pedagogical Practices Questionnaire (PTITPPP) was used to determine the proficiency levels of the participants in the use of hardware and software. Qualitative data was analyzed using abstracts, full papers, reflection journals, and field notes. Results revealed that pandemic largely influenced the upskilling of teachers’ digital skills and competence and the use of digital technology as evidenced by the high proficiency levels in the use of hardware and software and extensive use of varied digital platforms, applications, & teleconferencing tools. Findings show that the Covid-19 leveraged the teachers' needs to upgrade and expand their digital competence and apply them to deliver remote teaching and learning. Further studies on the effect of these digital technologies on the students’ learning gains and losses during the pandemic is recommended.
Implementation of Data Mining on a Secure Cloud Computing Over a Web API Using Supervised Machine Learning Algorithm

Ever since the era of internet had ushered in cloud computing, there had been increase in the demand for the unlimited data available through cloud computing for data analysis, pattern recognition and technology advancement. With this also bring the problem of scalability, efficiency and security threat. This research paper focuses on how data can be dynamically mine in real time for pattern detection in a secure cloud computing environment using combination of decision tree algorithm and Random Forest over a web Application Programming Interface (API). We are able to successfully Implement data mining on cloud computing without any direct interaction with data warehouse and without any terminal involve by using combination of IBM Cloud storage facility, Amazing Web Service, Application Programming Interface and Window service along with a decision tree and Random Forest algorithm for our classifier. We were able to successfully bypass direct connection with the data warehouse and cloud terminal with 94% accuracy in our model.
Deep learning

The set of algorithms designed for modeling a task by using multiple levels of nonlinear and nested operation units called artificial neurons falls under the umbrella of deep learning. Deep neural networks have found massive applications in the biomedical as, for instance, biomedical images and gene-expression analysis, genomics, and transcriptomics. This chapter introduces the artificial neuron model and some bases related to feedforward artificial neural networks. We will also discuss one of the central aspects of the popularity and power of deep learning, which is how a new latent representation of the input is learned by the network. Finally, we will provide a taste of the architecture of CNN.
Future Role of Bank Advisors and Traditional Bank Branches in the Age of Digitalization—An Empirical Investigation

Due to increasing digitalization, the German banking sector is undergoing a massive period of change.This threatening challenge is massively influenced by demographic change, supervision and regulation, the low-interest phase and technological progress.In the last twenty years, mergers of regional banks, staff reduction measures and the closure of their traditional bank branches have been the order of the day.Banks in Germany are therefore forced to deal with the introduction of omni-channel concepts.The aim of the research is to make a statement on the extent to which the two distribution channels "bank advisor" and "traditional bank branch" will still play an active role for regional banks in Germany in the future.The results of standardized interviews with bank experts (N = 43) in Germany form the core of this study.This paper summarizes the findings of the quantitative research descriptively.Furthermore, the article critically examines the academic discussion on the digital transformation of banks in Germany, especially savings and cooperative banks.This article is structured as follows: Introduction, Literature Review, Methodology, Results and Discussion, Conclusions, Limits and Directions for further Research.The results of this article can be useful for researchers and bank practitioners to identify and utilize the strategic change potential under omni-channel aspects due to the digital transformation.
PMNS for Efficient Arithmetic and Small Memory Cost

The Polynomial Modular Number System (PMNS) is an integer number system which aims to speed up arithmetic operations modulo a prime <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> . Such a system is defined by a tuple <inline-formula><tex-math notation="LaTeX">$(p, n, \gamma, \rho, E)$</tex-math></inline-formula> , where <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> , <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> , <inline-formula><tex-math notation="LaTeX">$\gamma$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\rho$</tex-math></inline-formula> are positive integers, <inline-formula><tex-math notation="LaTeX">$E\in \mathbb {Z}[X]$</tex-math></inline-formula> , with <inline-formula><tex-math notation="LaTeX">$E(\gamma) \equiv 0 \pmod p$</tex-math></inline-formula> . In (Didier, <i>et al.</i> 2020) conditions required to build efficient AMNS (PMNS with <inline-formula><tex-math notation="LaTeX">$E(X)=X^{n} - \lambda$</tex-math></inline-formula> , where <inline-formula><tex-math notation="LaTeX">$\lambda \in \mathbb {Z}\setminus \lbrace 0\rbrace$</tex-math></inline-formula> ) are provided. In this paper, we generalise their approach for any monic polynomial <inline-formula><tex-math notation="LaTeX">$E\in \mathbb {Z}[X]$</tex-math></inline-formula> of degree <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> . We present new bounds and highlight a set of polynomials <inline-formula><tex-math notation="LaTeX">$E$</tex-math></inline-formula> for very efficient operations in the PMNS and low memory requirement. We also provide AMNS and PMNS modular multiplication implementations, for a prime of size 256 bits, in classic C. We also provide, for the same prime, the first implementation taking advantage of the SIMD <monospace>AVX512</monospace> instruction set. The <monospace>AVX512</monospace> PMNS is 72 % faster than its AMNS counterpart (classical C version). This version presents a more than 60 % speed-up in comparison with the state-of-the-art Montgomery-CIOS modular multiplication of the <monospace>GMP</monospace> library.
Constraint Pushing Multi-threshold Framework for High Utility Time Interval Sequential Pattern Mining

This paper aims to detect high utility sequential patterns including time intervals and multiple utility thresholds. There are many algorithms that mine sequential patterns considering utility factor, these can find the order between the items purchased but they exclude the time interval among items. Further, they consider only the same utility threshold for each item present in the dataset, which is not convincing to assign equal importance for all the items. Time interval of items plays a vital role to forecast the most valuable real-world situations like retail sector, market basket data analysis etc. Recently, UIPrefixSpan algorithm has been introduced to mine the sequential patterns including utility and time intervals. Nevertheless, it considers only a single minimum utility threshold assuming the same unit profit for each item. Hence, to solve the aforementioned issues, in the current work, we proposed UIPrefixSpan-MMU algorithm by utilizing a pattern growth approach and four time constraints. The experiments done on real datasets prove that UIPrefixSpan-MMU is more efficient and linearly scalable for generating the time interval sequences with high utility.
Text Summarization in Hindi Language Using TF-IDF

Text summarization has become the main source of interest for researchers from the past few years due to the gigantic amount of information available on the Internet. It is impossible for humans to manually summarize such a large amount of information and get precise and meaningful summaries in less time. Hence, an automatic text summarizer is needed to get the job done in less time. The aim of the research was to create a medium via which the Hindi language text could be summarized and used potentially. In this paper, we have used the extractive method for achieving the goal.
Recognition of APSK Digital Modulation Signal Based on Wavelet Scattering Transform

The amplitude phase shift keying (APSK) employs as an efficient digital modulation scheme in modern satellite communication. Many researchers, in the field of automatic modulation recognition, entice to the automatic modulation of APSK signals. This paper exhibits a new algorithm based on machine learning, for APSK modulation recognition, using wavelet scattering transform. The algorithm handles three orders of APSK signals, 16APSK, 32APSK, and 64APSK. The scattering framework is performed with two filter banks to extract the scattering coefficients (SC) that have been applied to three well-known machine learning techniques, support vector machine (SVM), k-nearest neighbors (KNN), and Naïve Bayes (NB). The proposed method attained high accuracies with a low signal-to-noise ratio (SNR) and outperformed the existing works.
SCS-Gan: Learning Functionality-Agnostic Stylometric Representations for Source Code Authorship Verification

In recent years, the number of anonymous script-based fileless malware attacks, software copyright disputes, and code plagiarism issues has increased rapidly. In the literature, automated Code Authorship Analysis (CAA) techniques have been proposed to reduce the manual effort in identifying those attacks and issues. Most CAA techniques aim to solve the task of Authorship Attribution (AA), i.e., identifying the actual author of a source code fragment from a given set of candidate authors. However, in many real-world scenarios, investigators do not have a predefined set of authors containing the actual author at the time of investigation, i.e., contradicting AA's assumption. Additionally, existing AA techniques ignore the influence of code functionality when identifying the authorship, which leads to biased matching simply based on code functionality. Different from AA, the task of (extreme) Authorship Verification (AV) is to decide if two texts were written by the same person or not. AV techniques do not need a predefined author set and thus could be applied in more code authorship-related applications than AA. To our knowledge, there is no previous work attempting to solve the AV problem for the source code. To fill the gap, we propose a novel adversarial neural network, namely SCS-Gan, that can learn a stylometric representation of code for automated AV. With the multi-head attention mechanism, SCS-Gan focuses on the code parts that are most informative regarding personal styles and generates functionality-agnostic stylometric representations through adversarial training. We benchmark SCS-Gan and two state-of-the-art code representation models on four out-of-sample datasets collected from a real-world programming competition. Our experiment results show that SCS-Gan outperforms the baselines on all four out-of-sample datasets.
Using Electronic Medical Records to Develop a Reference Standard for Low Prevalence Disease Validation Studies: A Pertussis Case Study

Objective: Pertussis surveillance remains essential in Canada, but ascertainment bias limits the accuracy of surveillance data. Introducing other sources to improve detection has highlighted the importance of validation. However, challenges arise due to low prevalence, and oversampling suspected cases can introduce partial verification bias. The aim of this study was to build a reference standard for pertussis validation studies that provides adequate analytic precision and minimizes bias. Study Design and Setting: We used a stratified strategy to sample the reference standard from a primary care electronic medical record cohort, with the last 50% allocated to optimize precision using predicted cases from count models. We incorporated abstractor notes into definite, possible, ruled-out, and no mention of pertussis classifications.Results: We abstracted eight hundred records from the cohort of 404,922. There were 208 (26.0%) definite and 261 (32.6%) possible prevalent cases. Classifications demonstrated a wide variety of case severities. During optimization, the predicted width of 95% confidence intervals for sensitivity ranged from 12.4% to 32.8%. Conclusion: When conducting validation studies for low prevalence diseases like pertussis, this stratified sampling strategy can be used to develop a reference standard using limited resources. This approach mitigates verification bias while providing sufficient precision and incorporating a range of case severities.
Towards a Generic Architecture of Context-Aware and Intentional System

Context-awareness is a vital character of IoT and pervasive systems, helping to understand user needs with minimum human intervention. Furthermore, the context-awareness must be completed with Intentionality to improve the accuracy of delivering suitable service for a user. Most proposed approaches focus on the context’s static nature but don’t give enough importance to the user’s intention and context’s dynamic nature. Then bringing Intentionality to context-awareness methods is an open-ended research topic with many challenges and opportunities for innovation. The key issue of our research is enhancing the relationship between the user’s context and intention based on a situation-centered approach. This paper proposes a situation-centric meta model by providing practitioners with architecture to develop a context-sensitive intentional service, improving the relationship between the user’s context and the intention. An illustrative example is provided to explain the proposed architecture using E-tourism as a case study.
LayerLSH: Rebuilding Locality-Sensitive Hashing Indices by Exploring Density of Hash Values

Locality-sensitive hashing (LSH) has attracted extensive research efforts for approximate nearest neighbors (NN) search. However, most of these LSH-based index structures fail to take data distribution into account. They perform well in a uniform data distribution setting but exhibit unstable performance when the data are skewed. As known, most real life data are skewed, which makes LSH suffer. In this paper, we observe that the skewness of hash values resulted from skewed data is a potential reason for performance degradation. To address this problem, we propose to rebuild LSH indices by exploring the density of hash values. The hash values in dense/sparse ranges are carefully reorganized using a multi-layered structure, so that more efforts are put into indexing the dense hash values. We further discuss the benefit in distributed computing. Extensive experiments are conducted to show the effectiveness and efficiency of the reconstructed LSH indices.
Elderly Life Inquiry Toolkit: Empathy Expanding in Elder-Friendly Technology Design

ICTs have become deeply ingrained in social life. Design for elder-friendly technology is gaining traction in China to reduce obstacles for older people’s access to smart gadgets and services. Designers must increase their awareness of heterogeneous images of the old and elder technology usage in everyday life to create technological solutions that are meaningful to the aging population. Young designers encounter empathetic difficulties in design practice due to a lack of experience with aging, qualitative research abilities, and training. This research proposed a human-centered Elderly Life Inquiry toolkit to bridge this gap. The toolkit establishes a qualitative research framework of ‘Exploration-Interaction-Reflection,’ together with thematic guidelines, to assist young designers in engaging with the elderly utilizing various empathy techniques. It enhances designers’ empathy for older adults in everyday life, develops qualitative research abilities, and incorporates insights into designs. According to a series of empirical experiments and prototyping involving young student designers and older participants, the qualitative research method of face-to-face intergenerational communication is more suited to Chinese elders’ social preferences. This research demonstrated the process of developing the toolkit and the potential of the research techniques during the COVID-19 outbreak. At last, we emphasize the importance of supporting young designers to engage with diverse older adults, valuing the creativity of multidisciplinary participants, and focusing on the relationship between older life and technology for empathy in technology design.
Data Narratives: Aesthetic Activation of Urban Space Through Augmented Reality

This chapter discusses Data Narratives, a commissioned augmented reality artwork resulting from a period as artist in residence with Dublin City Dashboard. Data Narratives focused on working with city data to create hybrid artistic representations of Dublin’s ongoing housing affordability crisis, acting both as activist artistic engagement with the socio-political-economic space of the city and aesthetic activation of urban space through augmented reality. As data describes and defines so much of our digital every day, the project and residency programme asked how it could be leveraged as a medium for artistic creation and how could art supply new insights into these data and the life worlds they describe? Additionally, the project explored collaborative methodologies working in AR, increasingly important for artists producing complex AR works with the latest generation AR toolkits. The chapter gives an account of this project detailing its ambition to utilize AR art to build AR prototypes that over-layered city neighbourhoods with a series of cell phone-based data-driven AR narratives. Contextual location-based narratives visualize and engage complex issues. The process of building an artistic AR layer built on civic data is detailed, and the paper discusses its provision of a contextual layer that promoted reflection, informed debate, supported decision making, while connecting city residents with their city through renditions of its data.