Reviewerly/exHarmony huggingface dataset:
Evaluation of Station Timing Errors from Demeaned Arrival Time Delays

ABSTRACT We present an analytical solution for the evaluation of timing errors at seismological stations. The method makes use of differential P- and S-wave arrival time measurements demeaned over a network that recorded a set of densely located seismic events. In this configuration, one can assume coincident P and S ray paths between sources and receivers, and cancel out dependencies associated with absolute event origin times, event locations, and P- or S-wave velocities in the problem. Relative timing errors can be obtained by linear inversion, using only a limited amount of input data: differential P- and S-wave arrival times, and a local VP/VS ratio. By setting at least one reference station in the network, supposed to be devoid of any timing error, one can retrieve reliable timing errors for other stations. We validate the approach against synthetic and real data. We also analyze the sensitivity of results on errors in the input data. Although picking uncertainties do affect the variability of estimates, we also identified a significant bias when an incorrect VP/VS ratio is used. However, this bias can be reduced if one uses the optimal VP/VS value that minimizes the root mean square of travel-time residuals. Application of the method to a collection of manually picked arrival times for the 2002–2003 Tricastin, France, earthquake, swarm allowed us to identify nonstationary timing errors from tenth to tens of seconds during the monitoring campaign.
Bayes' approach of linear regression to modeling the human development index in Indonesia

Regression analysis is one of the data analysis techniques that can be used to examine the correlation between two variables, namely dependent variable and independent variable. It’s can be used to determine the parameter estimation of linear regression models are; the method of least squares or ordinary least square (OLS), Maximum likelihood estimation (MLE), and the Bayes method. Bayes' method defines the parameter as a random variable that describes the initial comprehension of the parameter before the observation was initiated and elucidated in an initial distribution refer as the prior distribution. The prior distribution used in this study is the pseudo prior distribution. The Data used in this study is secondary data, namely human development index (HDI) data in 2020, which was obtained from the website of the Central Statistics Agency (BPS). This study aims to estimate the regression model parameters using the Bayes method on the HDI data and the population data which adepts with information and communication technology (ICT) in Indonesia in 2020. The results of the specimen and analysis showed that population variables with ICT adepts have a significant effect on HDI variables. The results of the determination coefficient showed that 78.42% of HDI variables are affected by the population variables with ICT adepts while the remaining 21.58% are affected by other factors that have not been studied.&#x0D; Keywords: Bayes method, human development index, information communication and technology, linear regression, pseudo prior.MSC2020: 62F15
The Factors That Influence Students’ Choice of Dining-Out

The majority of students on campus get their sustenance from nearby food establishments.While picking a restaurant, they often take a number of aspects into account.Consequently, the purpose of this study is to investigate the elements that contribute to UiTM Dungun students' preference for dining off campus.In order to obtain data for this study, a questionnaire-based survey was done.Using convenience sampling, 205 students from UiTM Dungun were selected as responders, and all data was analysed using SPSS software.This research highlights six factors that influence dining out: price, food quality, meal variation, eating habits, ambiance, and technology or digital service.As a result, food quality was the most important factor that they considered, and the majority of participants chose to dine off campus because the food services off campus offered a greater variety of foods.Nevertheless, the most favoured meal service among students was hawkers.Restaurant operators should comprehend and be more aware of the aspects that influence students' restaurant choices and preferences in order to boost their sales and marketing efforts.Food service providers may want to consider enhancing their meal quality and expanding their menu selections.
A confidence-aware and path-enhanced convolutional neural network embedding framework on noisy knowledge graph

In recent years, Knowledge Graphs (KGs) have been widely used in applications such as search engines and Q&A systems. During construction, errors were inevitably introduced and KGs may contain many incorrect facts. However, most KG representation learning methods assume that all triple facts are correct, making KG noise detection a challenge. Although some knowledge representation methods are based on a confidence-aware framework, they are usually limited by the translation assumption in TransE and confidence calculation methods. To address the above problems, we propose a more accurate and effective confidence-aware and path-enhanced convolutional neural network knowledge embedding framework (CPConvKE). It makes use of structural, entity type, and rule information for knowledge graph noise detection and knowledge representation learning simultaneously. Specifically, for representation learning, we introduce a gating-based path embedding method to filter out noise while learning entity and relation embeddings. For the confidence evaluation, we propose a triple confidence estimator that uses entity type and rule information as prior probability and defines posterior probability based on the embedding. We evaluate the effectiveness of our model on the knowledge graph noise detection, completion, and triple classification tasks. The experimental results show that our confidence-aware model achieves significant and consistent improvements on all tasks, which confirms the ability of CPConvKE to detect noise and learn clean embeddings in a noisy knowledge graph.
Validation of a Strategic Intervention Material through Localization for Entrepreneurship Teachers

The primary goal of this paper is to verify a Strategic Intervention Material for Entrepreneurship Teachers Using Localization at Angono National High School for the school year 2022-2023.Utilizing descriptive correlational research and modifying Abad’s (2022) research instrument the ten (10) respondents found out that localized material as with high integrity, highly localized, and highly effective. Moreover, with regard to the assessment done by the two group of respondents, it indicated that both does not have significant relation with each other. From a catena of studies on localization, this fortifies that localization when planned and executed properly will provide positive results.
Telecommunications regulation, mobile money innovations and financial inclusion

Purpose This study aims to assess how corporate telecommunication (telecom) policies follow telecom sector regulation in mobile money innovation for financial inclusion in developing countries. Design/methodology/approach Telecom policies are understood in terms of mobile subscriptions, mobile connectivity coverage and mobile connectivity performance, whereas mobile money innovations represent mobile money accounts, the mobile used to send money and the mobile used to receive money. The empirical evidence is based on Tobit regressions. Findings Telecom sector regulation positively influences mobile money innovations. From net influences, mobile subscriptions and connectivity policies moderate telecom sector regulation to positively influence mobile money innovations, exclusively within the remit of mobile money accounts because the corresponding net influences on the mobile used to send money and the mobile used to receive money are negative. The interactive influences are consistently negative, and hence, thresholds for complementary policies are provided to maintain the positive influence of telecom sector regulation on mobile money innovations. Originality/value This study has complemented the extant literature by assessing how corporate telecommunication policies follow telecommunication sector regulation in mobile money innovations for financial inclusion.
Design of a location-based augmented reality game for the development of key 21st century competences in primary education

The use of augmented reality games (ARGs) in education has gained increased attention from curriculum developers, teachers, and researchers in the past decade. Research findings show that ARGs can promote meaningful learning environments that foster key competences for the 21st century. This paper presents the design process of “EcoAegean”, an ARG for mobile devices, which was implemented in primary classroom environments to support the development of students’ key competences in the context of sustainability. The game was created using an open augmented reality software platform and its design was based on contemporary theoretical underpinnings regarding the use of such games in educational contexts. In the first section of the paper, we describe the design procedures of the learning scenario as well as the game itself. In the last section of the paper, we offer a set of critical insights on the design and implementation of mobile augmented reality games for the purpose of supporting students’ development of key 21st century competences.
USE OF M-LEARNING IN HIGHER SCHOOL FOR THE STUDY OF FOREIGN LANGUAGES AND HISTORICAL DISCIPLINES

Higher education in Ukraine has faced new difficulties in 2023 (martial law, evacuations, relocations, need for adaptation, and tension), but distance learning has been offered. Therefore, problems such as introducing innovation, enhancing the effectiveness of this type of training, and improving distance education methods remain important. Enhancing students' enthusiasm, initiative, and sense of responsibility in the process of learning and its practical implementation are crucial components of teaching activities. Utilizing mobile apps for educational purposes is a way to rev up and diversify the educational process. Young people can be encouraged to use smartphones and tablets for educational reasons, because they frequently use them for communication and entertainment.
An IoT Dashboard Monitoring First Responders’ Health and Environmental Data during HAZMAT Emergencies

The goal of the REaCH (Real Time Emergency Communication System for HAZMAT Incidents) project was to develop a real-time interactive dashboard that captures and displays first responders' health data and environmental exposure information to an incident commander during a HAZMAT (Hazardous Materials) incident. The REaCH application monitors individual health parameters and helps Incident Commanders evaluate if an individual needs to be removed from the scene due to their health status being compromised. The REaCH application utilizes the latest IoT technology integrating multiple IoT sensors that capture human and environmental data, using a real-time communication network. Our demo presents the key features and design practices of our IoT dashboard. Practitioners and researchers will find these insights useful when developing similar IoT dashboard applications for emergency management.
A Software Project That Partially Failed: A Small Organization That Ignored the Management and Technical Practices of Software Standards

We explore what went wrong when a small organization ignored the proven practices of a software engineering standard and what should have been done to meet the needs of this organization if the practices of software engineering standards have been used.
A Multiperspective Intervention Path Study of Employment Expectancy in the Era of Big Data

The increasingly large employment group in colleges and universities has brought huge employment pressure to the society. Therefore, improving the level of professional ability of college students, providing students with personalized career development direction planning, and helping students to establish a correct concept of career selection are feasible ways to solve the structural contradiction of college students’ employment. The arrival of big data era provides new development opportunities for employment work in colleges and universities. Big data drives the high-quality development of precise employment in colleges and universities, which is inevitably required to reach the precise requirements of work development, information mastering, platform docking, information pushing, and help work. The traditional employment guidance work is backward in means and poor in timeliness, which leads to poor intervention effect on students’ employment expectation. With the deepening of digital campus construction and the development of big data technology, massive educational data has been accumulated but not reasonably utilized. Therefore, in this paper, we take the massive student data accumulated in digital campus as the research object, based on the research method of convolutional neural network, to explore the hidden personalized information of students behind it, predict their future career development direction, and provide scientific and technological support for the work of university education and teaching. Thus, the architecture classical DenseNet was improvised to avoid gradient disappearance and guarantee the classification accuracy, thereby targeting to reduce the number of connections. The study also proposed a 2-DenseNet model, wherein the attention module was embedded into 2-DenseNet followed by the conduction of training and validation on the university employment dataset. The experimental results show that the method proposed in this paper could predict the appropriate career development direction timely and effectively based on multiple perspectives of students’ comprehensive quality. This would enable the students to plan and adjust their employment expectations leading to disseminating of efficient employment guidance in colleges and universities.
Fabric Blockchain Design Based on Improved SM2 Algorithm

As one of the most widely used federated chains, hyperledger fabric uses many cryptographic algorithms to ensure the security of information on the chain, but the ECDSA cryptographic algorithm used in the fabric system has backdoor security risks. In this paper, the authors adopt SM2 algorithm to replace the corresponding ECDSA algorithm for blockchain design based on fabric platform. Firstly, they optimize the part of SM2 signature algorithm process with inverse operation and effectively reduce the time complexity by reducing the inverse operation in the whole process, and the experimental results show that the improved SM2 algorithm improves the signature and verification efficiency by about 5.7%. Secondly, by adding SM2 algorithm template and interface to the BCCSP module of fabric platform to realize the shift value of SM2 algorithm and compare the performance with the native fabric system, the network startup time is reduced by about 29%. The experimental results show the effectiveness of the improved SM2 algorithm, and also the performance of the optimized fabric system is improved.
Autonomous Driving Peripheral And Central Vision Region Selection For Semantic Segmentation

There has been a lot of development in autonomous cars recently and it's anticipated that fully autonomous vehicles will be on the road soon. Autonomous vehicle understanding of the surrounding environment is one of the most important autonomous driving tasks and this task is done by using deep CNN-based segmentation which imposes a remarkably high computational load. This paper proposes a novel method to reduce the computation time by mimicking the human eye vision in terms of dividing the wide field of view into two zones the peripheral vision and the central vision. The peripheral vision included the entire field of view of the camera while the central vision was selected based on the path and lateral acceleration of the vehicle whether it is moving to the right, left or straight ahead. The results showed a great improvement in the computational time.
Hybrid Feature Based 6D Pose Tracking under Binocular Vision for Automated Micro-assembly

6D tracking is important in robotic based assembly of the MEMS. This paper presents a 6D pose tracking method using hybrid feature for automated micro-assembly. Firstly, the micro-assembly system which mainly comprises of a parallel positioning robot that can largely rotate around three axes, a serial 6-axis robot for manipulation, and a binocular microvision modular is built. To realize tracking of the 6D pose under large rotation, a hybrid feature based tracking scheme that can be used under multi-view is developed. Experiments are conducted to verify the performance of the proposed method. The results show that the tracking performance can be effectively improved by simultaneously using the point feature and line feature. Besides, the averaged translation error and rotation error are smaller than 0.031 mm and 0.3 degree, respectively.
A Retrofitted Dynamic Window Approach with Pivot Point Control for Maneuvering Inland Vessels on Constrained Surfaces

Inland waterways play an important role in the transportation of goods and passengers. But maneuvering vessels in this type of restricted area can be challenging due to the limited maneuverability and the presence of obstacles. Hence, these areas can be abstracted as constrained surfaces. The dynamic window approach (DWA) is a path planning strategy for real-time navigation in constrained environments that considers dynamic constraints. While the pivot point concept is a useful technique which analyze the spot that vessels rotate around. Concerning these two concepts, this paper proposes a real-time local path planning algorithm named retrofitted dynamic window approach (RDWA) for maneuvering inland vessels on constrained surfaces. The algorithm retrofitted the DWA to satisfy the control behavior constraints stemming from the limited maneuverability. Further, the pivot point technique is leveraged to enable the vessel to pivot around desired points. The algorithm is evaluated by simulations, which demonstrate the approach can pivot the vessel around a number of pre-defined points for more than 90 degrees and form an arc motion trajectory in less than three minutes.
Multi-mode Planning for a Low-cost Robot Effective Exploration

Exploring unknown environment and creating an accurate map is one of the most important capabilities for a autonomous robot. For a robot to efficiently explore unknown environment, it needs to make optimal decisions to select a proper direction to goals and create waypoints to guide the robot to move towards goals based on gathering information about the environments. This work aims to develop an effective exploration approach for a low-cost mobile robot. We proposed a multi-mode exploration strategy for a robot to work in different scenarios like corridors, rooms, tunnels, open spaces etc. Our approach considers the exploration planning hierarchically-global targets and local targets (also named frontier points) to reduce the cost and improve efficiency. The directions to global targets are restricted by virtual grids and sequenced waypoints of subspace. The local planning is maintained with frontier exploration and sample based planning methods. The planner also obtain the semantic information about environments and obstacles for mapping strategy. Our approach follows the divide-and-conquer thought which makes it possible for autonomous mobile robots to perform exploration effectively with limited computing resource such as tiny modular robots or swarm robots.
A Hybrid Deep Learning Network for Sentiment Analysis on SemEval-2017 Dataset

Nowadays, the vast amount of information on the internet enables people to be informed of other thoughts and ideas quickly and get an insight to use in their decisions. Knowing how people feel about a person or event can significantly impact the decisions of individuals and organizations. With the advent of social networks and their high popularity, most people tend to share their opinions on the internet. Analyzing these feelings on social networks, as a good representation of society, can help make organizational decisions and forecast important events. Therefore, processing large volumes of information is a challenge that many researchers have considered. This study aims to present a new approach to analyzing emotions and detecting the polarity of the opinions of Twitter social network users using deep learning algorithms. The use of deep learning networks on textual data requires pre-processing and text conversion into vector space, so textual data will be transformed into vector space using the word embedding structure. This study analyzes the Twitter social network due to its data availability and significant application in emotion classification and uses the SemEval-2017 Task 4 dataset. We propose a hybrid model of the LSTM, CNN, and GRU networks, using CNN to extract the text features and the LSTM and GRU networks to preserve long-term dependencies. Moreover, we handle an imbalanced dataset using data augmentation techniques. Then we evaluate the performance of the proposed model using multiple metrics. The results show that our proposed method is about 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> better than previous related works.
An Approximate Method for Spatial Task Allocation in Partially Observable Environments

Multi-robot task allocation has many applications in the real world. Robots often have noisy or local sensor readings, making their workspace partially observable. This paper proposes a partially observable spatial task allocation algorithm, called POSA, that extends the subjective self-absorbed view of E-FWD, a task allocation algorithm for a fully observable environment. POSA uses Partially Observable Monte-Carlo Planning (POMCP) to evaluate the value of the successor belief states. Simulations show that POSA can reach the performance of E-FWD, even though it has partial observability rather than full observability. POSA also has a better convergence rate because it uses Monte-Carlo simulations that estimate the value of suitable locations of search space and does not have to evaluate the value of all parts of the search space.
Fashion Compatibility Learning Via Triplet-Swin Transformer

Owing to the rising standard of living, personal appearance, mainly matching clothes, is essential to people. Because the right clothes not only can beautify their appearance directly but can also increase their self-confidence. This study aims to help users find a matching pair of clothes by considering the intricate details to choose suitable and compatible clothes. In this paper, increasing the efficiency of feature extraction is very important because fashion has a complicated concept, and the extraction of features such as the overall shape, design, and texture of clothes can significantly impact understanding and learning the compatibility of clothes. Therefore, suitable global features can help a lot in understanding the compatibility of clothes. Transformers can extract global features better than convolution networks. We use Swin Transformer networks to extract the image features. We have trained a Triplet-Swin network to learn fashion compatibility, which achieves better accuracy than previous methods. We evaluated our model with AUC and FITB metrics and the Polyvore Outfit dataset.