Reviewerly/exHarmony huggingface dataset:
An IoT-Based Earthquake Warning System for Smart Cities

Earthquakes are the most common natural disasters that occur in India. An earthquake warning system minimizes damage and saves countless lives. A seismic wave analysis helps develop an early warning system. The bigger the earthquake, the stronger the shaking. Hence, magnitude determination is critical to developing an earthquake early warning system. The chapter deals with detecting earthquake magnitude by identifying the individual magnitude of earthquakes. An early warning system can be effectively implemented by the proposed method, along with high-end processors and the IoT (internet of things), which has the ability to collect and transfer data over networks with no manual intrusion. The proposed early earthquake warning (EEW) system can be used to support the development of smart cities so earthquake-prone zones are made less susceptible to disaster.
Interfaces of Medication Reminder Applications: An Analysis Aimed at the Elder Age

Despite how technologies are, nowadays, becoming integrated to several branches of society, there are still many groups with trouble to follow these technological advancements. Among them, the group that most likely suffers from this difficulty are the elders. An area strongly benefited by technology is health, currently counting with multiple mobile applications for smartphones, designed to upkeep the users’ health. In this context, the current paper proposes an analysis of interfaces from medication reminder apps focused on the elderly. The following techniques were implemented: An Usability Test through Exploration (UTE) to evaluate easiness in learning by exploration, the System Usability Scale (SUS) method to ascertain the usability level of said applications and the Heuristic Evaluation of their Interfaces. With those techniques, it was possible to build a solid benchmark to evaluate the level of elder-friendliness of an app in the given context. Results from the case study have shown that the apps lack a design focused on the elder public, which might have led to troubles during the tests performed with volunteers. The expert-based results have also found general interface issues on these apps that may need to be improved.
Research on Model Designinig Process Education by Interactive Methods

In product design education, it is necessary to design the existing modeling method considering the possibility that the problem in interaction between other people and product occurs in all kinds of places. As a preliminary investigation, we thought that we might be able to offer new value after interactive methods practical using." Consider the user when devising methods of gathering and providing information", "Make it a seemingly everyday product that gets people excited when displaying information", and "Figure out how to prevent the user from becoming overly passive toward the product" were the three points given attention during development. In addition, we made a travel map by contrasting my experience by conducting a hearing survey to find out what the student’s work is, which is struggling in writing and hard work in modeling. The purpose of this study is to establish a foundation through modeling support materials, to challenge free molding, to raise awareness of modeling, and to improve the formative power.
Game Cinematography

Although work is in progress to get a more realistic 3D simulation of a real environment, digital cinematography in games has so far delivered what it has promised in terms of high fidelity and realistic experience for the users. Therefore, with the available technology, we have investigated how video game cinematography could further be integrated into game design through the use of realistic camera lens distortion? To answer this question, our research develops a test bed using a camera model that simulates spatial distortion generalizing real-world lens behaviour. We implemented this camera in a test bed that simulates three use cases in games. Through this test bed, we tested our simulations with a survey sample of 21 people and made observations of how it impacted the game-play experience. Through these observations, we measured two metrics that comprise the game- play experience - agency and experience. From our observations and discussion, we demonstrate the use cases where realistic lens simulation and spatial distortion in game-cinematography could improve the game-play experience. Our research shows that further research should be made in exploring how game play experiences are shaped by the implementation of cinematographic techniques in a interactive game setting.
Towards the Ontology of Becoming in Self-tracking Research

This article contributes to research perspectives on the topic of proactive self-tracking. Self-tracking, referring to the employment of near-body biometric technologies such as activity wristbands and sleep tracking devices that collect data on bodies, is a popular practice of health-related life management, and thus an object of interest across a variety of research fields. However, we argue that much of the research throughout the various fields subscribes, to some degree, to a bifurcating ‘ontology of being’ through which the human and the technical are separated from each other at the level of ontological presumptions. In this article, we draw from a range of (new) materialist philosophy and discussions in the field of science and technology studies, and propose a shift towards a processual ‘ontology of becoming’ in self-tracking research. We suggest some theoretical and conceptual fine-tunings through which research may be better equipped to investigate the processuality of actors and multidirectional flows of agency in self-tracking practices. While our main contribution is theoretical, we will also illustratively analyze two interview narratives of self-tracking in order to demonstrate the different types of research worlds that open up depending on the ontological perspective taken.
A Behavior-Based Covert Channel Based on GPS Deception for Smart Mobile Devices

A physical behavior-based covert channel can leak information between two parties by purposely altering the application's internal states or behavior produced by a human. In this paper, the design of a physical behavior-based covert channel is presented by using GPS(Global Positioning System) deception for smart mobile devices, and an implement is provided. Our design gives well consideration to non-handshaking, offline transmission, and low suspicion. We demonstrate our implement's practicability by making experiments with different kinds of character strings. According to the comparison between the experimental trails and the real trails of university male students, our design meets the demands of stealthy and accuracy.
Assessment of Collaboration and Feedback on Gesture Performance

This paper proposes gesture performance as one main channel for assessing collaboration skills, while multiple users solve a problem collaboratively on a tangible user interface. Collaborative problem solving incorporates two dimensions, complex problem solving and collaboration. Thus, the technology-based assessment of collaborative problem solving includes assessing both problem solving and collaboration skills. Particularly, for assessing collaboration skills, we consider gesture performance as an important indicator. We differentiate between physical 3D mid-air gestures and manipulative gestures; for the latter, we developed a gesture recognition application using Kinect. The method we follow for object and gesture recognition is to merge the logging files from our tangible interface software framework (object recognition) with the Kinect log files (gesture recognition) in one file. The application can analyze the number of object manipulations with respect to timing axis, subject/participant, and handedness.
Developing QR Authentication and Fingerprint Record in an ATM Interface Using User-Centered Design Techniques

ATMs are widely used around the world, which leads to the development of new functionalities that need to consider usability. For this reason, the following article presents the development of graphical interfaces for ATM of BBVA Continental Bank, which follows a user-centered process design. In the first place, we gathered information about our target user. Then, we developed a prototype based on the previous information and the needs of the bank, which also took into account expert’s feedback. Finally, real users in the bank itself validated the prototypes.
Data augmentation for intra-class imbalance with generative adversarial network

In classification tasks, the accuracy of classifiers depends on training data. It is known that inter-class imbalanced data degrade the classification accuracy. Previous approaches tend to use data augmentation to solve inter-class imbalance, but the possibility of intra-class imbalance has been ignored. In this paper, we propose a novel method to solve the intra-class imbalance with Generative Adversarial Networks (GAN). The key idea is to examine the distribution of training data in latent space. We experimentally demonstrate that the proposed method generates diverse images and improves classification accuracy on the CIFAR-10 dataset.
The Relationship between the Academic Performances in Required Major Courses and the Core Courses Achievement of Food Science and Technology Students

The purpose of this study is to measure the proportion of variation in the academic performance in required major courses form the subjects in core courses of Food Science and Technology students.Data were collected from a sample of 183 online reports of science students graduating from Food Science and Technology Program, Suan Sunandha Rajabhat University during the academic year of 2011-2017.Variables used to construct a regression model were grades of the subjects in core courses and average grades of the subjects in required major courses.The findings revealed that there is a positive significant strong linear relationship between the core courses achievement and the academic performance in required major courses.Organic chemistry, Physical chemistry, Analytical chemistry, Microbiology and Basic biology grades express their strong relationship with academic performance in required major courses.The highest of the proportion of variation in the academic performance is Analytical chemistry.
A Pragmatic Prototype to Enhance The Quality Pre-University Level Skills Towards Further Development of Human and Knowledge Attributes in the Region

The research based on knowledge-based information system, follows a pragmatic study of pre-university level educational learning outcomes and the skills gained by the students, to further assess and compare it with the required knowledge and skills needed at the entry gate of the university level education. It will identify the gaps between the two, consecutively synchronizing the attained efforts to further enhance the delivery of quality pre-university level education as desired, focused towards the core of human and knowledge development to sustain and further enhance country’s fiscal and structural growth over time. The research is needed to address the fact that most of the students received at the entrance gate of university level education, lack necessary basic knowledge and skills. If these deficient knowledge and skills are incorporated it could help develop their performance at the level of further higher education. The attainment of those deficient skills would also help boost the student’s ability and capability at the level of higher education, thereby contributing towards nation’s human and knowledge development across various sectors. Diverse factors that negatively affect the students and their performance during their higher education at the university level education are categorized and identified. In order to effectively address these factors, we propose a knowledge management system to efficiently scrutinize the core areas necessary to fill in the breach by incorporating appropriate attributes which would thereby contribute towards the country’s economic and structural growth. The outcomes and the end results of the research enforces a methodology to reform the structure of higher educational systems and consecutively the lower level educational systems to be sequentially aligned to the expectations and benchmarks of industry standards, through a top-down approach.
Development of a telepresence robot with adaptive shared controller

This study presents the design and control of a telepresence robot based on an omnidirectional mobile platform. A personal computer is used as a user interface for communication via internet to a laptop computer placed on the mobile platform. The laptop computer receives user’s commands to control the mobile robot. Collisions of robot with obstacle can still occur during remote controlling because of the limitation of a fixed camera on the robot and/or internet latency. We first use an artificial potential field to prevent these collisions, and then design an adaptive shared controller (ASC) to improve the performance. This ASC method modulates the weights of human control and assistance control online not only according to environmental factors, but also based on the user’s control performance. Meanwhile, we use the iterated closest point (ICP) algorithm to build a map for providing user functional reference. The experimental results show that ASC is efficient in both obstacle avoidance and user workload reduction.
RGB-D Indoor Object Recognition Algorithm Based on Fusion Convolutional Neural Network

Abstract Aiming at the problems of insufficient network fusion and low detection efficiency in current object recognition using RGB-D images, a recognition algorithm based on the medium-level layer-by-layer fusion of dual-channel networks is proposed. First of all, the RGB and Depth networks are trained with ten labelled RGB-D indoor objects respectively, and then determine the fusion coefficients according to the identify accuracy of two types networks. Finally, two kinds of features are merged in convolutional layers step by step to obtain the final weights. By testing on the challenging NYU Depth v2 dataset, we found that the recognition accuracy of our method is 92.85%, and average detection time is 61.03ms per image. Through comparison experiments, we got the conclusion that average accuracy of the RGB-D layer-by-layer fusion network is 5.22% higher than that of the RGB network.
What Health Information Are Consumers Seeking? A Comparison Between Two Types of Online Q&amp;A Sites

Online questioning and answering (Q&A) sites have emerged as an alternative source for serving individuals' health information needs. Despite studies on analyzing user-generated content in online Q&A sites, there is an insufficient understanding of health consumers from the perspective of health-consumer types, information needs and number of questions. Additionally, empirical comparisons of different Q&A platforms are scarce. This research investigates types of health consumers, information seeking needs, and number of questions for asking questions across two types of online Q&A platforms. Empirical analyses of 624 heath questions collected from Yahoo! Answers and WebMD reveal several important differences. In comparison, there were more questions about adverse drug reactions on WebMD, and more questions about seeking similar experiences on Yahoo! Answers. The findings have design implications for online Q&A sites to better support health information seeking.
Metrics Driven Architectural Analysis using Dependency Graphs for C Language Projects

The highest share of cost for a software product is software maintenance. Identifying the quality merit of the software architecture is extremely vital as the quality directly relates to software maintenance. A good design always exhibits good quality characteristics because it is directly related to good architecture. Although C language is a major language in the software industry, few studies investigate the quality of the architecture in C language. This study aims to evaluate the quality of C language projects in a quantifiable form by focusing on dependency graphs, associated metrics, and software architecture. In particular, this study (i) formulates the architecture representation of C projects, (ii) determines the metrics capturing the quality of architecture, (iii) defines code smell and metrics relations and (iv) conducts an empirical analysis on 58 C projects. We show which metrics derived from dependency graphs can detect architectural issues and verify their relation to software architecture quality.
On Delegatability of a Certificateless Strong Designated Verifier Signature Scheme

A strong designated verifier signature (SDVS) is a special variant of digital signatures, since it only allows a designated recipient to verify the signer’s signature. The transcript simulation property of such signatures also prohibits a designated verifier from arbitrarily transferring his/her conviction to any third party. When implemented in certificateless cryptosystems, a certificateless SDVS is unnecessary to manage public key certificates and deal with the key-escrow problem of conventional identity-based systems. In 2014, Shim pointed out a crucial security property called non-delegatability for SDVS schemes. This property states that anyone should not be able to generate a valid SDVS without obtaining either the signer’s or the verifier’s private key. In other worlds, a non-delegatable SDVS scheme must ensure that any malicious adversary cannot forge a valid signature even if he/she has gotten the shared secret value between a signer and an intended verifier. In this paper, we first demonstrate that a previously proposed efficient certificateless SDVS scheme is vulnerable to the delegatability attack and then further propose an improved variant.
Designing and Developing Architectures to Tangible User Interfaces: A “Softwareless” Approach

This paper main goal is to explore the architectures of TUIs, discussing an integrated approach of developing this kind of interface as a generic architecture model. To do so, research in the state of art was made, using search engines and considering the RSL methodology. In the end, a new architecture will be proposed, without the use of a software layer, therefore a “softwareless” approach.
Rationality Measurement for Jadex-Based Applications

Nowadays, measurement becomes a primordial technique in any software project. By measurement, we mean, the process of assigning a value to an attribute. However, measurement must take into account the specificities of novel software paradigms. Hence, we propose in this paper some metrics to measure the rationality of agents. Despite the importance of the rationality as one of reasoning characteristic; there is no measure that targeted this characteristic. The proposed metrics are applied on Jadex platform which is one of well-known agent platforms. In addition, a tool is developed to measure automatically the proposed metrics. The developed tool is based mainly on aspect-oriented programming.
Comparing the effort and effectiveness of automated and manual tests

This paper presents three case studies that compare the effort and effectiveness of automated versus manual testing, in the context of a multinational services organization. Effort is measured in terms of the total test time, which includes script creation and test execution in the case of automated testing, and comprises test execution and reporting in the case of manual testing. Effectiveness is measured in terms of the number and severity of defects found. The software under test is a set of Java web applications. The testing process was carried out by two testers within the organization. Our results show that automated testing needs a higher initial effort, mainly caused by the creation of the scripts, but this cost can be amortized in time as automated tests are executed multiple times for regression testing. Results also show that automated testing is more effective than manual testing at finding defects.
FPGA-Based Large Constraint Length Convolution Code Encoder Verification

The convolution code with large constraint length plays an irreplaceable role in deep space communication and ultra-low frequency communication. Therefore, it is very important to find and test convolution code with large constraint length. Convolutional codes are widely used in deep space communication systems because of their high coding gain and simple and reliable encoders. The performance and implementation difficulty of convolutional codes mainly depend on the constraint length of the decimal codes and the coding efficiency. Enlarging and improving the coding gain of convolutional codes will greatly increase the complexity of decoders. The method of mathematical derivation and verification by mathematical tools is not suitable for convolutional codes with large large constraint length.. In order to improve the efficiency of inspection, the method of parallel multi-core computing needs to be introduced into the evil code test. Tests show that the FPGA-based parallel inspection method can improve the test efficiency by geometric multiples.
Recognition of Odia Handwritten Digits using Gradient based Feature Extraction Method and Clonal Selection Algorithm

This article aims to recognize Odia handwritten digits using gradient-based feature extraction techniques and Clonal Selection Algorithm-based (CSA) multilayer artificial neural network (MANN) classifier. For the extraction of features which contribute the most towards recognition from images, are extracted using gradient-based feature extraction techniques. Principal component analysis (PCA) is used for dimensionality reduction of extracted features. A MANN is used as a classifier for classification purposes. The weights of the MANN are adjusted using the CSA to get optimized set of weights. The proposed model is applied on Odia handwritten digits taken from the Indian Statistical Institution (ISI), Calcutta, which consists of four thousand samples. The results obtained from the experiment are compared with a genetic-based multi-layer artificial neural network (GA-MANN) model. The recognition accuracy of the CSA-MANN model is found to be 90.75%.
Experimental Research in the Field of Creation of Automated Information System for Component Management of Academic and Methodological Complex of Discipline

The paper presents the results of experimental studies in the field of automated information system creation. These results show possible problem solutions of automation of educational documentation development and support. Software realization options of automated information system are considered as adjusting the existing specialized software designed for processing text documents. Besides, in practice the approach to the automated process of compiling an interrelated documents set on the basis of a single reference database using AuthorIT.