Reviewerly/exHarmony huggingface dataset:
Colombia's cyberinfrastructure for biodiversity: Building data infrastructure in emerging countries to foster socioeconomic growth

Societal Impact Statement Colombia is a “megadiverse” country with vast natural resources. A history of recent conflict means that information is only now being collected on the natural capital of regions that were previously unexplored. Better access to data, tools, and expertise is required for evidence‐supported decisions on the conservation of these resources. The development of a bespoke cyberinfrastructure could help fulfill this need by providing access to digital resources in a collaborative cyberenvironment. We outline key priorities and develop a reference framework for building cyberinfrastructure in Colombia. This framework could be applied to other fields and countries to promote knowledge exchange, scientific innovation, and socioeconomic growth.
COMMUNITY RESILIENCE AND ADAPTATION STRATEGY OF FLOOD DISASTERS IN TRUCUK DISTRICT, BOJONEGORO REGENCY

&lt;p&gt;This study aims to determine (1) the level of community resilience to flooding in Trucuk District, Bojonegoro Regency in 2018; (2) community adaptation strategies to flood disasters in Trucuk District, Bojonegoro Regency in 2018; and (3) efforts to improve resilience and adaptation to flood disasters.&lt;/p&gt;&lt;p&gt;The population in this study were all villages in Trucuk District, Bojonegoro Regency. The sample used was flood-affected communities in each village by purposive sampling technique. Data collection techniques using survey methods. Data collection was carried out by questionnaire and interview. Data analysis uses scorring techniques.&lt;/p&gt;&lt;p&gt;The results of the study are as follows. (1) Resilience of the Trucuk Subdistrict community against flooding has moderate resilience (52.86 - 62.38) and high resilience (65.89 - 75.13). the distribution of resilience is moderate namely there are 6 villages consisting of the village of Kandangan, Sumbang cucumber, Kanten, Pagerwesi, Padang, and Guyangan. For high resilience there are 6 villages consisting of Sumberjo, Mori, Tulungrejo, Trucuk, Sranak, and Banjarsari villages. (2) Adaptation strategies undertaken by the Trucuk District community in dealing with floods are physical and non-physical adaptations. The physical adaptation carried out by the community and the government is to improve physical conditions, such as elevating houses, repairing irrigation networks, building dams, and building embankments along rivers. Non-physical adaptation carried out by the community and the government is the existence of an early warning system in detecting the danger of flooding and community preparedness in dealing with floods, such as mutual cooperation and mutual assistance in all matters before and after the flood. (3) efforts to improve resilience and adaptation to flood disasters.&lt;/p&gt;
Human Action Recognition through the First-Person Point of view, Case Study Two Basic Task

In this study, a human motion dataset is built and developed based on indoors and outdoors actions through a bounded-onhead camera and Xsens for tracking the motions.The key point here to structuring the dataset is utilized to set the sequence of a Deep Neural Network and order an arrangement of frames in the performed task (washing, eating, etc.).As a final point, a 3D modeling of the person suggested at every frame centered with the comparable structure of the first network.More than 120,000 frames constructed the dataset, taken from 7 different people, each one acting out different tasks in diverse indoor and outdoor scenarios.The sequences of every video frame were 3D synchronized and segmented 23 parts.
Cybersecurity Infrastructure and Security Automation

AI-based security systems utilize big data and powerful machine learning algorithms to automate the security management task.The case study methodology is used to examine the effectiveness of AI-enabled security solutions.The result shows that compared with the signature-based system, AI-supported security applications are efficient, accurate, and reliable.This is because the systems are capable of reviewing and correlating large volumes of data to facilitate the detection and response to threats.
Analysis on Skin Colour Model Using Adaptive Threshold Values for Hand Segmentation

The hand gesture recognition system is the hottest topic for the human-machine interaction and computer vision fields.The hand gesture recognition system is still a challenging research area in computer vision for human-computer interaction because of various device conditions, various illumination effects, and very complex background.The recognition of hand gestures used in various application areas: such as sign language recognition, man-machine interaction, human-robot interaction, and intelligent device control and many other application areas.The robust detection of hand in hand gesture recognition system has become a challenging task due to clutter background, dynamic background, and various illumination conditions in real-world conditions.Segmentation is the partioning/separating the foreground hand region from the background region in an image.Segmentation is also pre-processing steps of the hand gesture recognition system.The recognition accuracy will increase if the hand region correctly detected.So, hand region detection is the main important step for the hand gesture recognition system.
The 6th International Workshop on Artificial Intelligence for Requirements Engineering (AIRE'19)

Presents the title page of the proceedings record.
The Effects of Knowledge-Transforming Text on Elementary Students’ Declarative, Procedural Knowledge, and Motivation in Environmental Learning

In environmental learning, declarative and procedural knowledge is needed to to improve understanding of concepts and problem solving.The text used in KTT contains descriptions and arguments to understand declarative and procedural knowledge.This study aimed to investigate the effect of applying knowledgetransforming text (KTT) on declarative, procedural knowledge, and students' presentation skill as well as their motivation towards learning with KTT applied in environmental education material.The study applied control group design.Prior to treatment, homogeneity tests were carried out with one way ANOVA test.Furthermore, in the experimental class instructional strategy armed with KTT was in place, the result of which was then analyzed by several quantitative analyses.These analyses included (1) normality test with a non-parametric one-sample Kolmogorov-Smirnov Test; (2) Mann-Whitney U test non-parametric statistic to compares the means between unrelated groups on some continous, and (3) significance tests.The results showed that KTT posed significant effect on declarative and procedural knowledge yet it insignificantly affected student presentation skills.The significant effects of KTT learning on declarative and procedural knowledge was supported by content space, rhetorical space, and corrective elements in KTT.Quantitaive descriptive analysis was carried out by interpreting the percentage of student motivation as investigated by questionnaires on KTT learning.Students were relatively motivated towards KTT and hoped to learn to use KTT.
Cyclic Cumulant Based on Communication Signal Multilayer Neural Network Modulation Pattern Recognition

Abstract After we deduces and simulates the cyclic spectrum theory of MPSK and MQAM modulated signals. The cyclic spectrum of the modulated signal is preprocessed and the original feature set is formed as the input data of the CNN. Feature extraction of cyclic spectrum from convolution and subsampling layers of CNN, so that the modulation signal can be effectively identified within and between classes. The experimental simulation results show that the classification and recognition algorithm of communication signal modulation mode proposed in this paper has better modulation recognition accuracy than other traditional modulation recognition algorithms in the case of low SNR. And when the signal to noise ratio is lower than 5db and higher than -5db, the modulation pattern recognition accuracy can be reached up to 92%.
Non-Stationary Representation for Continuity Aware Head Pose Estimation Via Deep Neural Decision Trees

The problem of image-based head pose estimation attracts intensive attention due to a large number of applications such as face analysis and attention modeling. Existing methods often convert head pose estimation into the pose classification problem, but ignored the non-stationary appearance change brought about by the equally distributed bins. This paper targets the head pose estimation problem via deep neural decision trees, where the non-linear property of the representative appearance is learned together with the bin classification probability. First, we use Convolutional Neural Network (CNN) to get pose related features. Second, we apply the Fully Connected (FC) layer on the learned features to extract branch weight for each Euler angle and the representative values for each bin. Third, we employ neural decision tree on the branch weight to get bin classification probability. To explicitly characterize the relationship between the adjacent pose intervals, we embed continuity of the head angles into the tree architecture by constructing the bridge-tree. The final estimation is obtained via a weighted sum between the estimated bin probability and the representative bin values. We evaluate our methods on different public datasets including Pointing’04, Chinese Academy of Sciences-Pose, Expression, Accessory, and Lighting (CAS-PEAL) and Biwi Kinect Head Pose and find that, the proposed method outperforms as compared to state-of-the-art. Besides, we leverage the template marching based alignment for data preprocessing and demonstrate its superiority over traditional alignment methods on the task of head pose estimation.
Mobile Phone Utilization for Learning: Physics Teachers' Perception

The use of mobile phones for learning is still becoming a debate in Indonesia. Many schools forbid their students to bring mobile phones to school. This study was aimed to describe the perceptions of physics teachers in Lampung Province regarding the use of mobile phones to study physics. Data was collected from 39 physics teachers from various districts in Lampung Province. The Likert scale with a range of 1 to 5 was used. Descriptive quantitative data analysis was carried out. The results showed that most teachers agree /strongly agree: (1) to utilize mobile phones in learning physics both inside and outside of the classroom, (2) the tendency of students to use mobile phones make the physics learning process efficient. It could strengthen and improve the quality of the learning process. Almost all physics teachers agree /strongly agree with mobile phones usage, because: (1) it could turn the learning process from teacher-centered into student-centered and from content-oriented learning to process-oriented learning, (2) it could improve efficiency and productivity in the learning process. However, they agreed that the use of mobile phones by students could not replace the teacher's role, so physics teachers needed a more sophisticated concept of cellular learning to be implemented
Line Matching of Point Clouds Based on Encoded Asymmetric Boundary

Feature extraction and matching of point clouds is an important issue of 3-D data processing. In this letter, the matching of the corresponding encoded asymmetric boundaries (EABs) is proposed, which is based on linear features, and the boundary line of planes is attained by Freeman differential coding and Hough transform, with two-level constructed indexing structure; EAB is not affected by rotation, translation, and scaling factors within the threshold of distance and angle means the achievement of the corresponding boundary lines matching simultaneously. A group of progressive experiments is undertaken to evaluate the robustness and efficiency of the proposed method. The experimental results have shown that the proposed method greatly improves the line matching efficiency and accuracy of point clouds, and it can be directly applied in the subsequent registration.
Effect of Dispersion on Classification of Different Datasets Using Multiplicative Neuron Model

The classification of nonlinear problems, single Multiplicative Neuron model is used. Several different databases like Fisher’s Iris database, wiconsin Breast Cancer, Mammographic Mass and Pima Indian have selected for study. The measure of dispersion has been found of all datasets by measuring standard deviation and range of dataset. The classification results of all problems compared and comparative analysis has been made of all datasets, considering several elements of Neural Network such as number of epochs, cost function, MSE and misclassification rate. After comparing various performance parameters mainly misclassification rate with measures of dispersion, it is found that misclassification rate of multiplicative neuron model depends only patterns of datasets. Seeing the results of study, it can be said that classification rate does not depend on the measure of dispersion (Standard deviation and Range) of datasets.
An enhanced hybrid image encryption algorithm using Rubik’s cube and dynamic DNA encoding techniques

Image encryption is among the most active solutions to protect confidential pictorial information.However, to design a strong image encryption algorithm with no recognizable pattern, the researchers in this field have to enrich the confusion and diffusion properties.This study proposes an efficient hybrid system that combines two techniques.First, we propose a modified version of Rubik's Cube technique for scrambling colored image pixels to achieve fast confusion.This technique not only scrambles the position of image pixels but also scrambles the color channels.Then, dynamic DNA encoding algorithm is used to encrypt the pixel's values.DNA encoding rules are used in conjunction with a secret key.We propose to select the DNA rules dynamically to enhance the security level.Five fidelity metrics are employed to assess the capability of this system.These are PSNR, SSIM, NPCR, Entropy, and CCA.The results indicate that the proposed system enhances the general security requirements with enriched confusion and diffusion properties of the encrypted image.
Using NLG for speech synthesis of mathematical sentences

People with sight impairments can access to a mathematical expression by using its L A T E X source.However, this mechanisms have several drawbacks: (1) it assumes the knowledge of the L A T E X, (2) it is slow, since L A T E X is verbose and (3) it is error-prone since L A T E X is a typographical language.In this paper we study the design of a natural language generation system for producing a mathematical sentence, i.e. a natural language sentence expressing the semantics of a mathematical expression.Moreover, we describe the main results of a first human based evaluation experiment of the system for Italian language.
Denoising The Wireless Channel Corrupted Images Using Machine Learning

Digital communication depends on channel coding for the integrity and correct reception of the data. The traditional communication techniques ignore the context and content associated with the received data while mitigating the channel effects. Image transmission in digital communication have many major constraints. Image quality degrades over wireless channel due to limited characteristic of transmitted data. To mitigate the effects of the noisy channel on the images different image denoising techniques are used such as DCT, Convolutional Denoising Autoencoders, DWT, BM3D, FFT, NLM and Deep Learning. Main objective of this paper is to study the application of machine learning in digital communication to correct errors and remove the effects of channel degradation. This will help us improve the energy consumption, resource utilization, and the Bit Error Rate. It will enable us to communicate with low bandwidth and using minimum resources. Machine learning for denoising the image has attracted substantial attentions because of its high denoising performance. We have constructed a Convolutional Neural Network that will denoise the image which is corrupted by the noisy channel. The existing traditional models only denoise the image for a specific noise type (Gaussian noise) and certain noise level σ = 25, but our trained network denoise the image for unknown noise level. By using the methods of batch normalization and residual learning the denoising performance is increased. Experiments prove that our trained model displays high efficiency in image denoising. The proposed network is trained in MATLAB and Python with the help of GPU computing which accelerates neural network's performance. The application areas of this paper mainly are in the domains related to digital communications. This include wireless communications, optical communications and line communications. However, as the wireless communications (especially underwater wireless) is extremely error prone, so this domain will be highly benefited. The applications can include military communications, Underwater Communication and Noisy Industrial Communication.
Complementarity of Reading from Paper and Screen in the Development of Critical Thinking Skills for 21st-century Literacy.

The skill of reading undergoes dramatic changes due to the change of reading interface readers are exposed to. Readers who want to be active participants of knowledge society need to perceive it as more than just a receptive skill.&#x0D; The study aims to assess the condition of homo legens, diagnose what kind of reading interface preferences characterize 21st-century readers, how they respond to texts considering reading both digitally and in print, accepting or viewing critically the underlying ideology of the text.&#x0D; The analysis of the collected data attempts to determine if the reported preferences are conducive to the development of critical thinking skills for 21st-century literacy, which include understanding complex ideas, evaluating evidence, weighing alternative perspectives and constructing justifiable arguments.
User Experience and Usability in Agriculture – Selected Aspects for Design Systems

The paper focuses on the analysis of the applicability of usability and UX methods in the development of applications intended for use in the agrarian sector. In addition to an overview of methods and approaches suitable for this purpose, the process of advanced adoption of the UX methods in the development of agrarian software products is also described. The article also discusses ways to enable partially automated, time-efficient, and cost-effective solutions for the creation of interfaces for agricultural applications. Possibilities of future development and technologies related to the usability of products in agriculture are also outlined. The article thus covers the concept for optimizing the development of agricultural products. The main contribution is the input analysis for the future development of the agrarian design system. From such a synthesized system design, it is then possible to create application interfaces that should have seamless usability and good UX.
Methodical foundations of the use of project-based technologies in teaching physics to students of technical specialties of higher education institutions

Methodical foundations of the use of project-based technologies in teaching physics to students of technical specialties of higher education institutionsThe presented work is devoted to the issues of using project technologies of training in higher education institution based on information technology.The training of highly qualified competitive specialists is the main task for all higher educational institutions.In other words, a graduate of a higher educational institution should design highly efficient, technically advanced engineering systems, analyze the effectiveness of a project in comparison with other projects.The question arises of how to prepare such a specialist in the conditions when the current educational system often lags behind the processes taking place in the global space.In this regard, the authors attach special importance to mini-projects that can be carried out in a short time, while these projects build students' skills such as group work, teamwork management, project reporting, design and research skills.The paper also proposes a general structure of the educational project for one semester.This describes the experience of using the computer program Electronics Workbench version 512 in teaching physics to students of technical specialties.The questions of the effectiveness of innovative creativity of students in the performance of tasks of the mini-project in the course of physics are considered on the example of the simulation of the operation of oscillatory circuits -serial and parallel.
Information System Design Based E-Commerce in Food Marketing

Information system based e-commerce is the most important method in food marketing in term of promotion costs and automatic data without human error. The system serves consumers by providing an easy and effective transaction.Information system based e-commerce has proven to satisfyconsumer needs. Therefore, this research aims to design a website-based information system marketing which is better than manual system applied in CV.Sari Kelapa Group. CV. Sari Kelapa Group is an individual company that develops into a large company and was founded in 1967s engaged in the production of Gresik food producers. Pudak is the first product of the company. Promotion at CV. Sari Kelapa Group, It is a fact that unavailability of appropriate promotional media in promoting the products are obvious. Furthermore, the approach uses prototype research and descriptive qualitative by giving interview to the subject. The results show that the website is able to overcome the short comings of the manual system applied by CV.Sari Kelapa Group and recommend a complete feature of online chat.
Primary Pupils’ Use of Social Media to learn ESL

The next generation of school-going pupils of elementary level will soon be made up of children from generation Alpha, born from the year 2010 to 2025. These technology-savvy young children will soon be dominating the classrooms demanding for digitalised instructional experiences. Technology-infused learning experience has been a prevalent concept in the education field in recent years. Many studies have been carried out to explore how social media and technology assists second language learning in the contexts of secondary and tertiary education. However, little is known about how elementary pupils learn English as a second language (ESL) using social media platforms, primarily in the Asian context. Therefore, the objective of this study is to investigate how Chinese primary school pupils learn English on social media. Drawing on the original data from a questionnaire survey and document analysis, the findings revealed that the research participants lack the experience of learning English on social media sites although they have access and the necessary competency in handling those sites. The implication derived from this study is that it would be advisable for teachers to steer and plan suitable instructional practices on social media platforms for elementary aged ESL learners in line with their learning preferences and needs.