Reviewerly/exHarmony huggingface dataset:
Architecture Optimization of Convolutional Neural Networks by Micro Genetic Algorithms

Convolutional Neural Networks (CNN) are novel techniques with significant performance in object detection and classification. An open research problem on CNN is the automatic finding of adequate architectures, which is usually done by hand. Metaheuristic algorithms are techniques that find optimal solutions in heuristic manner to problems where the knowledge is limited or almost nonexistent, such as finding optimal CNN architectures. In this chapter, we propose a framework that utilizes the micro genetic algorithm to find CNN architectures in the shortest possible time. The proposal is tested over three simple study cases known by the research community (MNIST, MNIST-Fashion, and MNIST-RB), and compared against two different frameworks from the literature: psoCNN, and simple genetic algorithm. The results show a better performance of the architectures found by our framework in terms of accuracy and processing time.
A critique of the bounded fuzzy possibilistic method

In a recent paper published in this very journal, the “Bounded fuzzy possibilistic method” (BFPM) was proposed. We point out that there are some critical flaws in the said algorithm, which makes the results presented therein highly questionable. In particular, the method does not generate meaningful cluster membership degrees and fails to converge when run on some well-known benchmark data sets.
dSubSign: Classification of Instance-Feature Data Using Discriminative Subgraphs as Class Signatures

Applications like customer identification from their peculiar purchase patterns require class-wise discriminative feature subsets called as class signatures for classification. If the classifiers like KNN, SVM, etc. which require to work with a complete feature set, are applied to such applications, then the entire feature set may introduce errors in the classification. Decision tree classifier generates class-wise prominent feature subsets and hence, can be employed for such applications. However, all of these classifiers fail to model the relationship between features present in vector data. Thus, we propose to model the features and their interrelationships as graphs. Graphs occur naturally in protein molecules, chemical compounds, etc. for which several graph classifiers exist. However, multivariate data do not exhibit the graphs naturally. Thus, the proposed work focuses on (1) modeling multivariate data as graphs and (2) obtaining class-wise prominent subgraph signatures which are then used to train classifiers like SVM for decision making. The proposed method dSubSign can also classify multivariate data with missing values without performing imputation or case deletion. The performance analysis of both real-world and synthetic datasets shows that the accuracy of dSubSign is either higher or comparable to other existing methods.
A Smart and Safe Robot System for Electric Monitoring

At present, as a place for power grid system application service operation, the daily operation and maintenance and inspection of the electric power equipment room still remain in the manual stage. This paper introduces the design method of an intelligent safe robot system for power monitoring. SLAM trackless navigation technology, deep learning-based visual recognition technology, face recognition model and intelligent voice interaction technology are applied in the system. The system can realize the daily automation and intelligent operation and maintenance of the power room. Such as: hardware equipment running state monitoring, power environment monitoring, asset inventory, etc.
Head Pose Classification Based on Deep Convolution Networks

Recently, the classification of the head pose has gained incremented attention due to the rapid development of HCI/HRI interfaces. The resoluteness of head pose plays a considerable part in interpreting the person’s focus of attention in human-robot or human-human intercommunications since it provides explicit information of his/her attentional target. This paper proposes a geometrical feature-based human head pose classification using deep convolution networks. An MTCNN framework is implemented to identify the human face and a ResNet50 layered architecture built to classify nine head poses. The system is trained with 2, 85, 000 and tested by 1, 15, 500 head pose images. The proposed system achieved \(90.00\%\) precision for nine head pose classes.
Analyzing the Effects of Lexical Cognates on Translation Properties: A Multivariate Product and Process Based Approach

The translation of cognates has received renewed interest in Translation Process Research (Oster (2017) Empir Model Transl Interpret 7:23; Hansen-Schirra et al. (2017) Predicting cognate translation. In: Hansen-Schirra S, Czulo O, Hofmann S (eds) Empirical modelling of translation and interpreting. Language Science Press, Berlin, pp 3–22) but tends to be relatively time-consuming due to the manual identification of cognates and their translations. On the basis of work by Heilmann (Profiling effects of syntactic complexity in translation: a multi-method approach. PhD thesis, 2021) and the structure of the TPR-DB, we devised a relatively simple way to determine the cognate status of ST words and detect literal, cognate translations of cognates. We assess the “cognateness” of ST items on the basis of formal (dis)similarity with aligned ST and TT words. We use these measures to show how the cognate status of ST words and the literal cognate-to-cognate translation of cognates affect properties of the translation product and process. Using multivariate statistics, we are able to show that a ST token’s cognate status is a determining factor of translation ambiguity. We also find evidence for cognates affecting reading and typing behavior during translation. Additionally, we observe a moderation effect of translation experience on behavioral measures when cognates are translated literally. We interpret the results in support of the monitor model (Tirkkonen-Condit (2005) Meta 50(2):405–414) and propose a refinement of the operationalization of literality (cf. Schaeffer and Carl (2014a) Measuring the cognitive effort of literal translation processes. In: Proceedings of the EACL 2014 workshop on humans and computer–assisted translation, pp 29–37).
Role of Computer Data Analysis in Applied Mathematics in the Internet Era

Abstract With the continuous progress of industrialization, the mechanical manufacturing industry has become a pillar industry of the national economy. Therefore, a large number of professional courses must be based on solid mathematical knowledge, which makes mathematics courses have an extremely important position in professional teaching. From the perspective of professional teaching, there is a big gap between the professional teaching and the professional teaching, but there is no need for students to give full play to the teaching objectives of mathematics. Through questionnaire survey, literature review and analysis, this paper finds out the loopholes and shortcomings of the current mathematics teaching mode. Students are not enthusiastic about mathematics, they do not identify with mathematics discipline from the bottom of their heart, and teachers can not cultivate students’ interest. I hope this paper can provide new ideas for mathematics education.
Digitizing the FlexIA Toolkit - Transforming a Paper-Based Method into a Flexible Web App

Digitization of everything is the main driver in today's economy. The constantly growing proliferation of digital tools makes it increasingly important to properly analyze and document which information is needed in which processes throughout a company. Information management competencies are a key enabler on the path to data generated value creation. The FlexIA toolkit described in this paper assists in such an analysis. One of its highlights is that it comprises both a method and a tool to investigate information flows within small and medium-sized companies. It focuses on pragmatic objectives and aims to empower workers in the domain of mechanical engineering to create an analysis independently. Starting out as paper-based prototype it soon became necessary to transform the tool into a digital web app. The first section of this paper illustrates the context in which the FlexIA toolkit was created and describes its core concepts. Afterwards, related work is reviewed and the FlexIA is delineated from other tools and methods. The third section discusses the motivation behind the FlexIA toolkit, elaborates on further use cases and reasons why a digital tool was needed. The major part of this paper covers the step wise process of digitizing the FlexIA toolkit. Mock ups and wire framing were used to capture the user requirements. Subsequently, implementation aspects are illustrated. A short discussion as well as an overview of future steps concludes this article.
Generating Dialogue Responses with Latent Lexical Meaning

Response generation is an important component of a dialogue system. One major drawback of the existing models is that they tend to generate generic responses, and specific and latent lexical information is ignored. However, in the real world, latent lexical meaning may better reflect the user’s background, which may be used to make our responses more coherent and interesting. To solve this problem, we propose a response generation model with latent lexical meaning. The model generates responses based on latent lexical meaning, which is identified by a multi-head attention mechanism, and is aligned with the output responses by lexical representation to guarantee the diversity and coherence of responses. Extensive experiments are conducted on the Sina Weibo Corpus. Experimental results show that the generated responses are evaluated as more interactive and diverse in both automatic and human evaluations.
Developing a Criteria-Based Evaluation Tool for User Experience Design that Balances Standardization and Creativity

Design systems are increasing in popularity, created to ensure a consistent aesthetic of graphics and interactions in websites and apps, and guide product development. They tend to include a set of standards, principles, and documentation. Due to their often-rigid requirements on structure and uniformity, traditional design systems can discourage creativity and customization. To forge a balance, the work develops a criteria-based evaluation tool, or ‘scorecard’ for assessing design components that incorporate principles of consistent, standardized practice, yet prioritize creative freedom. The evaluation scorecard allows inconsistencies to be managed in a collaborative and consensus-based manner. Users select parameters and metrics to evaluate the various elements of a design component. The tool calculates a score based on the number of parameters passed or failed. Scores below team-desired thresholds signal a need for further modification or redesign. Usability feedback using talk-aloud and surveys in a focus group format assess the ease of use and efficiency of the tool and identify gaps in functionality.
Improved Carrier Frequency-Shifting Algorithm Based on 2-FFT for Phase Wrap Reduction

The number of phase wraps that result from the carrier component can be completely eliminated or reduced by first applying a fast Fourier transform (FFT) to the image and then shifting the spectrum to the origin. However, because the spectrum can only be shifted by an integer number, the phase wraps of the carrier component cannot be completely reduced. In this paper, an improved carrier frequency-shifting algorithm based on 2-FFT for phase wrap reduction is proposed which allows the spectrum to be shifted by a rational number. Firstly, the phase wraps are reduced by the conventional FFT frequency shift method. Secondly, the wrapped phase with residual carrier components is filtered and magnified sequentially; the amplified phase is transformed into the frequency domain using an FFT, and then, the wrapped phase with the residual carrier components can be further reduced by shifting the spectrum by a rational number. Simulations and experiments were conducted to validate the efficiency of the proposed method.
Outlier Detection in Wireless Sensor Networks with Denoising Auto-Encoder

Anomaly detection is an effective approach of dealing with problems in decision making process. Rapid development in technology has elevated the requirement of resourceful detection system using machine learning, deep learning in order to detect new and advanced outliers. Most of the real time applications employed with wireless sensor networks, which are positioned in unkind and unattended atmospheres, where these situations turns to a major causes on the production of anomalous or low quality sensor readings. The erroneous and unreliable readings may increase generation of false alarms and erroneous decisions; hence it is essential to identify outliers resourcefully and exactly to make sure the authentic decision-making. In this paper, first simple cluster algorithm performed based o residual energies of sensor nodes and then denoising auto-encoder with Gaussian kernel applied on each cluster head to detect outliers. Experimental analysis shows that designed technique achieves high detection rate as well as low false alarm rate.
Designing Local Assessment Workshops and Web-Mobile Applications for Facilitating the Workshop

Rapid improvements in real-world accessibility conditions have enabled greater mobility of people with disabilities as well as senior citizens. However, the most recent information on accessibility conditions in areas such as suburbs is challenging to obtain quickly because of infrequent updates of this type of information along with local information upgrades. Though volunteer groups continuously check local conditions to ensure they are safe for people with disabilities, some of them face difficulty in maintaining the accessibility of information because of the shortage of members in such groups. In this study, we firstly proposed a framework named to design an assessment workshop and its supporting application based on the schemes of Problem/Project-based learning (PBL) and the ADDIE (Analyze, design, develop, implement, and evaluate). Based on the framework, we designed the workshop sessions of local assessments and supported mobile and web applications, then preliminarily evaluated this approach’s availability. The results suggested that our framework might appropriately enable facilitators to plan workshop sessions and consider how to facilitate, and also enable participants to learn the local conditions.
Assessing Small Institutions’ Cyber Security Awareness Using Human Aspects of Information Security Questionnaire (HAIS-Q)

For large financial institutions, the available resources allocated to cyber security greatly exceed the capabilities of smaller financial institutions, such as credit unions and community banks. However, the sensitivity of personally identifiable information (PII) and associated risks to the institutions and respective consumers in the event of security breaches are the same no matter the size of the financial institution. These facts when assessed in tandem yield a false equivalency: how can a smaller institution with significantly fewer resources keep consumer and company data secure at standards equivalent to large financial institutions? In this research, Human Aspects of Information Security Questionnaires (HAIS-Q) are voluntarily completed by six employees of a small financial institution and 25 undergraduate and graduate students enrolled in computing courses at a small four year collegiate institution and are quantitatively analyzed/discussed to evaluate potential relationships between employee knowledge, attitude, and behavior (KAB model).
Exploring the Applicability of User-Centred Design Practices in Rural Yemen

User-centred design methodologies are employed in many developing world design initiatives to pay more attention to and involve end-users in the design process. However, studies have shown that they need to be appropriated to the specific contexts. The paper presents an early design study that was conducted to investigate the applicability of user-centred design in the context of Yemeni culture at the example of a management system for a school in a rural area. Applied methods include rapid ethnography, focus group, interview, sketching techniques, paper prototyping, and high-fidelity prototyping. In contrast to the results of previous studies which indicate that users often do not see sufficient value in the design process itself, participants in this study found the process meaningful (although they were not fully engaged in each design activity). Some of the reasons may be in the nature of the design task and in the role of the researcher/designer and the schoolmaster who can be considered as key individuals. The present study contributes to the growing body of design-oriented field studies in the area of Human-Computer Interaction for Development.
Human-Computer Natural Interaction Design Practice Based on Unconscious Design Concept

Compared with the traditional industrial design, human-computer interaction design is a relatively young field. Throughout its decades of application history, the core of human-computer interaction design is from technology to business, and then from business to users themselves. As a change of human-computer interaction mode, human-computer natural interaction studies the interaction between human and machine —— “let computer adapt to human”. There are still some defects in the current human-computer natural interaction. The design practice is based on the research of user’s unconscious behavior, combined with the design framework of pervasive computing, to explore a kind of human-computer natural interaction design concept model to adapt to user’s behavior habits. The purpose is to realize the cognitive elderly’s instinctive interaction with the intelligent voice bracelet, meet the deep emotional and psychological needs of the cognitive elderly, establish a more consistent psychological logic of the cognitive elderly, make the product better serve the cognitive elderly, and bring new inspiration for the future research of human-computer interaction.
Development of Teaching Material to Design a Vehicle on Data Science in Junior High School Technology Education

The Society 5.0, advocated by the Japanese government in 2016, represents the society of the near future. In this context, the development and securing of human resources in mathematics, data science, and AI are urgent issues to promote technological innovation in the future. In this paper, a teaching material specializing in chassis of the vehicle performance in junior high school technology education was developed. The students manufactured their own teaching materials and evaluated them based on the data. The problems were identified and redesigned and remanufactured to solve them. The proposed class was practiced by students who were going to become teachers in a teacher training course, junior high school technology education. The results showed the educational effect of the material. In addition, a 13‐h lesson plan that is the basis of The Lower Secondary School National Curriculum Standard was proposed. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Sensitivity and robustness analysis in Bayesian networks with the bnmonitor R package

Bayesian networks are a class of models that are widely used for the diagnosis, prediction, and risk assessment of complex operational systems. Multiple approaches, as well as implemented software, now guide their construction via learning from data or expert elicitation. However, current software only includes minimal functionalities to explore the assumptions, quality of fit, and sensitivity to learned parameters of a constructed Bayesian network. Here, we illustrate the usage of the bnmonitor R package: the first comprehensive software for model-checking of a Bayesian network. An applied data analysis using bnmonitor is carried out over a medical dataset to illustrate the use of its wide array of functions.
Read the digital fingerprints: log analysis for digital forensics and security

Computers and other related computing systems – such as smartphones and tablets – are indispensable in our daily lives. Unfortunately, this attracts the attention of cyber attackers. Everyone is affected. Computer systems used in sectors as diverse as the defence industry, healthcare enterprises, the entertainment sector, banking and education have been targeted by illegal attacks.
Effect of Data Augmentation on the Accuracy of Convolutional Neural Networks

Data augmentation is one of the most famous techniques to increase the efficiency of a deep learning model. It involves using conceptual techniques to generate new data samples from existing ones. In the context of image processing, there are a plethora of options for performing data augmentation, for example, rotating images, changing hue, altering greyscale, etc. The main reason why data augmentation is used to reduce overfitting and increase the accuracy of a model. Convolutional neural networks (CNNs), conversely, are the most famous deep learning models used in the domain of image processing, image classification in particular. However, not much is known about the impact of various data augmentation techniques on the performance on various CNNs. In this paper, we analyse the effect of data augmentation on two different types of CNNs of varying dimensions. The aim of this study is to observe the impact of data augmentation techniques on the performance of CNNs of varying complexities. In order to bolster our findings, we used three different datasets for the use case of image classification and tried to compare the results for both CNNs. We analysed the performance of both types of CNNs over varying learning rates and epochs in order to compare our findings across varying hyperparameters.
Influence of hate speech messages on voters’ choice of candidate in Kwara State 2019 gubernatorial election

The Nigerian and some Asia country’s media landscape over the years has projected numerous political campaigns that are hate speeches directed at the perceived opposition candidates and political party. This study examined the influence of hate speech messages on voters’ choice of candidate in Kwara State, Nigeria in 2019 GE and was anchored on the agenda-setting theory. The study used a survey research design with a single cross-sectional research method. The study generated 464,393 registered voters as the study population. Using cluster sampling and purposive respectively to select 400 samples. The study, therefore, discovered that social media is the most channel of exposure by respondents on hate speech messages during the 2019 gubernatorial election in Kwara State with 66%. The study also found that the use of hate speech undermines the importance of political candidates with 72% in any given election. The study further discovered that the use of hate speech during elections influenced the voters by making them change from one candidate to the other with 82% thereby encouraging more votes against the opposition party. The implication of these findings has a strong influence on the way electorates undermine the importance of political issues and as such rendered political candidates and parties irrelevant with a critical effect on the credibility, fairness, and smooth running of any political landscape and the conduct of Nigerian elections in general. The paper proposes critical political education to electorates on voting behavior to promote good governance.