Reviewerly/exHarmony huggingface dataset:
Analyzing the similarity of protein domains by clustering Molecular Surface Maps

Many biochemical and biomedical applications such as protein engineering or drug design are concerned with finding functionally similar proteins, however, this remains to be a challenging task. We present a new imaged-based approach for identifying and visually comparing proteins with similar function that builds on the hierarchical clustering of Molecular Surface Maps. Such maps are two-dimensional representations of complex molecular surfaces and can be used to visualize the topology and different physico-chemical properties of proteins. Our method is based on the idea that visually similar maps also imply a similarity in the function of the mapped proteins. To determine map similarity, we compute descriptive feature vectors using image moments, color moments, or a Convolutional Neural Network and use them for a hierarchical clustering of the maps. We demonstrate the feasibility of our approach using two data sets: an ensemble of hand-selected proteins with known similarities used for verification and an ensemble of ketolase enzymes, where we analyzed the individual domains using our method. Our method is integrated in an interactive visualization application, which allows users to explore and analyze the results. It visualizes the hierarchical clustering and offers linked views that provide details for a comparative data analysis.
MobileNet for Differential Constellation Trace Figure

Radio frequency fingerprint technology is of great significance to the security of the Internet of Things system. When the signal uses I/Q modulation, the demodulated signal can be drawn on a two-dimensional plane, that is, constellation diagram. Slight differences in the manufacture and use of transmitters can cause the constellation to shift. Differential Constellation Trace Figure (DCTF) with device characteristics can be generated by special differential processing of demodulation signal. In this paper, the DCTFs of QPSK signal are used as radio frequency fingerprints. Radio frequency fingerprints are classified using MobileNet, a lightweight network that can be used for mobile and embedded devices. In the final simulation experiment, the accuracy of MobileNet V2 and MobileNet V3 are both over 95% when SNR is low. Comparing with the accuracy of CNN, MobileNet V2 and MobileNet V3 are better choices.
New Approaches of Multi-agent Vulnerability Scanning Process

There are various scanners used to determine the actual vulnerabilities, to measure their criticality level and the overall security level of the information infrastructure. These scanners have the same functionality and scanning principles. That is why the existing vulnerability scanning process has significant problems such as the necessary network modifications, the validity of infrastructure information and the complexity and unclarity of the scanning results. This paper provides new solutions to these problems. Also, the vulnerability scanner classification is described and related works were overviewed. The scanning process phases were analyzed and three main problems were identified. Authors compared vulnerability scanners by specially formulated criteria for the purpose of solving these problems. The unique sequence generation technique for multi-agent scanning and the unified scanning strategy are proposed as the new approaches for vulnerability scanning process. The necessity of new vulnerability assessment method is proposed which takes into account network environment of scanned hosts.
An Approach for Bengali News Headline Classification Using LSTM

Headline is called the soul of news. Headline carries a very important meaning. Generally, many people start reading the news after seeing the headlines. It is very important for the user to classify the headlines that he/she preferred. Classifying news type based on their headlines is a problem of text classification which lies under natural language processing (NLP) research. In different languages, there are many works done but none of them observed in Bengali. In our work we tried to visualize our very first approach to solve this problem. A LSTM-based architecture is used for solving this problem. A total of 4580 headlines are trained in our model. Finally, our model gives us 91.22% accuracy. The main challenge of our work is finding the right word vector. As far headlines are made up with different types of words and there is no similarity between any of them, so it is difficult to map them.
The Openness Hypothesis in the Context of Economic Development in Sub-Saharan Africa: The Moderating Role of Trade Dynamics on FDI

This study investigates the simultaneous openness hypothesis by assessing the importance of trade openness in modulating the effect of foreign direct investment (FDI) on economic dynamics of gross domestic product (GDP) growth, real GDP and GDP per capita. The focus of the study is on 25 countries in Sub-Saharan Africa over the period spanning from 1980 to 2014. First, trade imports modulate FDI to induce net positive effects on GDP growth and GDP per capita. Second, trade exports moderate FDI to generate overall positive impacts on GDP growth, real GDP and GDP per capita. Implications of the study are discussed, inter alia: (i) both FDI and trade infrastructures are necessary for FDI-focused measures to engender positive economic development outcomes in host communities and countries. (ii) Macroeconomic conditions that are relevant for promoting economic development are necessary for the interactions between trade openness and FDI to generate favorable outcomes in terms of GDP growth, real GDP and GDP per capita.
Polynomial Event Semantics: Negation

Polynomial event semantics is an interpretation of Neo-Davidsonian semantics in which the thorny event quantification problem does not even arise. Denotations are constructed strictly compositionally, from lexical entries up, and quantifiers are analyzed in situ. All advantages of event semantics, in particular, regarding entailment, are preserved. The previous work has dealt only with positive polarity phrases involving universal, existential and counting quantification. We now extend the polynomial event semantics to sentences with negation and negative quantification, including adverbial quantification, with attendant ambiguities. The analysis remains compositional, and does not require positing of non-existing entities or events.
Face Morphing Attacks: A Threat to eLearning?

Recently, the use of remote education via electronic media, i.e. eLearning, has increased due to the COVID-19 pandemic. To achieve secure and reliable identity verification in eLearning exams, it has been suggested to employ face recognition technologies for remote student authentication in online examinations. However, novel attacks on face recognition in eLearning systems have rarely been considered in the scientific literature.In this work, we investigate the feasibility of so-called face morphing attacks in eLearning systems. Such attacks can be launched in scenarios where students are authenticated via identity documents containing face images that are remotely presented prior to online examinations. In this relevant scenario, students are able to fool human examination and automated face recognition by morphing their face image with that of an accomplice, e.g. fellow student. Resulting morphed face images contain biometric information of both subjects contributing to it. Consequentially, an accomplice could take part in an online examination for a student with high probability of passing an identity verification unnoticed. We assess the vulnerability of a commercial and an open-source face recognition system to said attack. To this end, a realistic dataset of morphing attacks is collected. It is shown that automated face recognition in eLearning systems can be tricked with alarmingly high success chance.
Evolutionary and Swarm Optimization

This chapter introduces the basic evolutionary algorithms, including the canonical genetic algorithms, real-coded genetic algorithms, evolution strategies, genetic programming, ant colony optimization algorithms, particle swarm optimization, and differential evolution. In addition, memetic algorithms that combine evolutionary search with local search, and estimation of distribution algorithms that use a probabilistic model to generate offspring solutions will also be described. Finally, basic methodologies for solving multi- and many-objective optimization problems are introduced.
Vulnerability Analysis of Intellectual Property Rights Using Reverse Engineering: Based on Software D

To protect software piracy, software manufacturers use protective technologies such as license authentication. Despite this, attackers use software illegally by neutralizing the license authentication techniques by reverse engineering. We analyze the vulnerabilities of the software D’s license authentication techniques to find the underlying problems inherent in its existing protection techniques. The analysis reveals the code for license authentication in software D and can bypass license authentication by modifying the authentication result value or code flow. Moreover, this software provides license types such as single-user, family, educational, and corporate, and we demonstrated that all types of licenses were neutralized. In conclusion, addressing the fundamental vulnerabilities of the software D’s license authentication technology can improve the security of its license authentication technology, which is considered more robust in protecting the intellectual property rights of software.
Effectiveness of Transfer Learning in Autonomous Driving using Model Car

We have known that reinforcement learning, deep learning, and deep reinforcement learning effectively acquire action rules for the autonomous motion of objects. However, it is known that these learning processes require a large amount of learning time. Besides, we should consider the similarity of the environment between the training target and the test target. In actual autonomous driving, there is no such thing as driving only on a course that has been learned in advance. In this study, the autonomous driving of a model car is used as the experimental object. The training target for acquiring the learning model and the actual driving courses are changed. In this study, we report on the effectiveness of transfer learning using a model car as the basis for a learning model acquired by reinforcement learning.
The effect of slider design and length on user performance and preference of smartphone versions of the visual analogue scale

This study aims to investigate the effect of slider design and length on user performance and preference of smartphone versions of Visual Analogue Scale (VAS). Twenty-eight participants performed a task to set random target values with 8 smartphone versions of VAS: 2 slider designs (traditional design, modern design) × 4 slider lengths (4.3 cm, 5.8 cm, 10 cm landscape, 10 cm portrait). Experimental results showed that both slider design and length significantly affected the accuracy, task completion time and subject preference. Compared with the traditional slider design, the modern slider design showed significantly smaller bias in setting values, shorter task completion time, and higher subject preference. The slider length significantly affected all measures, and 5.8 cm was recommended due to small bias, short task completion time, dominant preference and excellent ability to closely fit the width of smartphone display with the portrait mode. These findings could provide mobile VAS and slider designers with useful references.
Tapping the Power of Social Media on Innovation Performance

Objective - This study investigates the relationship between social media and innovation performance among SMEs in Malaysia. This study also extends social media literature by investigating the underlying mechanism of open innovation in the relationship between social media and innovation performance. Methodology/Technique - A questionnaire was used to collect data from the respondents. A total of 173 samples from data collection were then used to test the hypotheses by using the SPSS and SmartPLS software. Finding - The result has revealed that social media has a significant effect on innovation performance. Besides, outbound innovation is also found to mediate the relationship between social media and innovation performance. Novelty - This study contributes to the literature on social media and innovation by providing new evidence regarding outbound innovation impact on performance among SMEs. It also provides a great idea of social media's importance to SME managers in improving innovation performance in an organization. Type of Paper - Empirical. Keywords: Social Media, Innovation Performance, Open Innovation, Smes, Malaysia JEL Classification: URI: http://gatrenterprise.com/GATRJournals/GJBSSR/vol9.2_4.html DOI: https://doi.org/10.35609/gjbssr.2021.9.2(4) Pages 143 – 151
Use of Virtual Mirror in Design of Individualized Orthopedic Supplies

The paper presents studies on a prototype of a virtual mirror application, intended to aid the design process of individualized orthopedic supplies, such as limb orthoses or prostheses. The application is a part of an automated design and manufacturing system, relying on 3D scanning and 3D printing for manufacturing of supplies adjusted to specific patients. The application itself is divided into two parts: the configurator and the mirror. The paper presents briefly a concept, a prototype application based on Kinect sensor and studies on a small group of patients, as well as results of a survey study. Certain inaccuracies of Kinect sensor were detected in the tests, preventing smooth user experience with the mirror. However, the presented approach was viewed in a favorable way by the test group – the concept of a virtual mirror is a promising solution that should be utilized in the design process of individualized orthopedic supplies. On the basis of observations and surveys, recommendations for future development were formulated.
A stitching algorithm for automated surface inspection of rotationally symmetric components

This paper provides a novel approach to stitching surface images of rotationally symmetric parts. It presents a process pipeline that uses a feature-based stitching approach to create a distortion-free and true-to-life image from a video file. The developed process thus enables, for example, condition monitoring without having to view many individual images. For validation purposes, this will be demonstrated in the paper using the concrete example of a worn ball screw drive spindle. The developed algorithm aims at reproducing the functional principle of a line scan camera system, whereby the physical measuring systems are replaced by a feature-based approach. For evaluation of the stitching algorithms, metrics are used, some of which have only been developed in this work or have been supplemented by test procedures already in use. The applicability of the developed algorithm is not only limited to machine tool spindles. Instead, the developed method allows a general approach to the surface inspection of various rotationally symmetric components and can therefore be used in a variety of industrial applications. Deep-learning-based detection Algorithms can easily be implemented to generate a complete pipeline for failure detection and condition monitoring on rotationally symmetric parts.
Procedural Patchwork: Community-Focused Generative Design for Quilting

We are developing quilt design generation software for Code Crafters workshops, which aim to broaden public awareness of computational thinking and build links between computer science and quilting. The software generates quilt designs, which will be manipulated and modified by workshop participants. Due to COVID, our workshops will be conducted remotely, so the software has been designed to increase the sense of community by providing opportunities for the types of social interactions that take place in colocated quilting workshops.
Prediction of Perceived Utility of Consumer Online Reviews Based on LSTM Neural Network

Perceived value is the customer’s subjective understanding of the value they obtain and is their subjective evaluation of the product or service they enjoy. This value is deducted from the cost of the product or service. In order to understand and predict the specific cognition of consumers on the value of products or services and distinguish it from the objective value of products or services in the general sense, this paper uses the in-depth learning method based on LSTM to establish a model to predict the perceived benefits of consumers. It is a challenging task to analyze the emotion of consumers or recognize the perceived value of consumers from various texts of online trading platforms. This paper proposes a new short-text representation method based on bidirectional LSTM. This method is very effective for forecasting research. In addition, we also use the attention mechanism to learn the specific emotional vocabulary. Short-text representation can be used for emotion classification and emotion intensity prediction. This paper evaluates the proposed classification model and regression data set. Compared with the baseline of the corresponding data set, the contrast of the results was 93%. The research shows that using deep neural network to predict the perceived utility of consumer comments can reduce the intervention of artificial features and labor costs and help predict the perceived utility of products to consumers.
Visualizing Interactions Between Solar Photovoltaic Farms and the Atmospheric Boundary Layer

The efficiency of solar panels depends on the operating temperature. As the panel temperature rises, efficiency drops. Thus, the solar energy community aims to understand the factors that influence the operating temperature, which include wind speed, wind direction, turbulence, ambient temperature, mounting configuration, and solar cell material. We use high-resolution numerical simulations to model the flow and thermal behavior of idealized solar farms. Because these simulations model such complex behavior, advanced visualization techniques are needed to investigate and understand the results. Here, we present advanced 3D visualizations of numerical simulation results to illustrate the flow and heat transport in an idealized solar farm. The findings can be used to understand how flow behavior influences module temperatures, and vice versa.
Personalized Human Activity Recognition using Hypergraph Learning with Fusion Features

Human activity recognition (HAR) is a promising field which has a wide range of applications in medicine, electronic forensics and Internet of Things. Until now, existing works generally focus on artificial extraction of statistical features or applying deep learning to extract deep features to perform activity recognition. However, most studies neglect individual differences among different users, which leads to performance decline of the trained model when applied to new users. In this paper, we propose a novel approach using hypergraph learning for personalized human activity recognition based on fusion features. Fusion features take advantage of deep features and statistical features, and also fuse the user's personalization factors to reduce the influence of individual differences. In the classification part, a hypergraph learning algorithm is used to recognize user's activities based on the fusion features. Experiments on the public dataset USC-HAD (11-class) and the self-collected dataset (6-class) show the proposed method has superior performance compared to existing methods, as well as greater potential for usage in personalized human activity recognition during daily lives.
Liver Cancer Trait Detection and Classification Through Machine Learning on Smart Mobile Devices

Machine learning techniques have provided a technological evolution in medicine and especially in the field of medical imaging. The aim of this study is to firstly compare multiple transfer learning architecture models such as MobilleNetVn (n = 1, 2), InceptionVn (n = 1, 2, 3, 4), InceptionResNet, VGG16 and NasNetMobile and provide a final performance estimation, using a variety of metrics, secondly, propose an efficient and accurate classifier for liver cancer trait detection and prediction, and thirdly, develop a mobile application which uses the proposed model to classify liver cancer traits into various categories in real-time. Magnetic Resonance Images (MRI) of mouse liver cancer of different origin were used as input datasets for our experiments. However, the required memory by the deployed Convolutional Neural Network (CNN) models on smart mobile devices or embedded systems for real time applications, is an issue to be addressed. Here, all the baseline pre-trained CNN models on the ImageNet dataset were trained on a dataset of MRI images of mice of different genetic background with genetically- or chemically- induced hepatocellular cancer. We present and compare all main metric values for each model such as accuracy, cross entropy, f-score, confusion matrix for various types of classification. Data analysis verifies that the proposed optimized architecture model for this task of liver cancer trait classification and prediction, the MV1-LCCP, shows a suitable performance in terms of memory utilization and accuracy, suitable to be deployed in the mobile Android application, which is also developed and presented in this paper.
Spikes in Agile Software Development: An Empirical Study

Spikes can be an essential component of the agile development cycle, because they assist the teams, for both technical and functional issues, to identify any uncertainty in a user story, leading to a more efficient solution to the problem. The use of spikes in agile software development (ASD) can enable organizations to produce quality software by employing the required technical expertise, planning the entire development cycle and ensuring that the client's requirements are adhered to. This study aims to examine the use of spikes in ASD. It explores the role, efficiency and efficacy of spikes in various software development domains through the different agile methods. An exploratory research design is adopted to achieve this purpose, whereby mixed methods are used to collect concurrently both qualitative and quantitative data from the experts recruited to the study. Through the survey, it establishes that the primary role of spikes is risk management through investigations to understand user stories and reveal any uncertainty. Conclusively, the study findings imply that spikes have become an essential tool for most agile teams in ASD. The efficiency and effectiveness that are reported show that the majority of experts in software development have realized the value of using spikes in their processes.