Reviewerly/exHarmony huggingface dataset:
Community Preparedness of the Fire Hazard in Jetis District, Yogyakarta City

Abstract This study aims: (1) to determine the level of community preparedness in facing fire hazards in the Jetis District of Yogyakarta City and (2) to understand the efforts to increase community preparedness to deal with fire hazards in the Jetis District of Yogyakarta City. This research uses descriptive methods with quantitative and qualitative approaches. In this research, we utilize the environmental approach as a tool. The indicator that we use here is based on the 2006 UNESCO / ISDR LIPI to measure the level of community preparedness. This research was conducted at Jetis District of The City of Yogyakarta. The population of this study was all family heads in Jetis Subdistrict, at least we came to 9282 families. The number of samples was determined using the Slovin formula and obtained 99 respondents. We chose the Proportional Random Sampling in three villages in Jetis Subdistrict as the sampling technic. The data was gained from the process of observations, structured interviews with standardized guidelines, in-depth interviews with three main resource persons, and documentation. The analysis technique that we used is the frequency table and descriptive analysis. The results show that: (1) the level of community preparedness in the Jetis Subdistrict in facing the danger of fire was in the "Less Ready" category (55.56%). The level of community preparedness on the knowledge and attitudes of the majority was in the category of “Ready” with 63.63%. The majority of emergency plan variables are in the unprepared category as much as 56.57%. The majority of disaster warning system variables are in the ready category that is 56.57%. Resource mobilization ability is a variable with respondents who are at the lowest level of preparedness, the majority are in the category of not ready as much as 76.77%. (2) the efforts to increase fire hazard preparedness in the community in Jetis District have been carried out, namely: conducting training and simulation of fire prevention, procurement of supporting facilities for fire prevention, making fire engines, and establishing disaster relief posts in Jetis District. Efforts that must be increased are: educating the public about the risks of fire hazards, increasing synergy between community organizations and local governments, and adding firefighting infrastructure.
Object Detection System in Blind Spot Dump Truck Area Using Fuzzy Logic with Sugeno Method

Dump Truck has an area that cannot be seen by the drivers, which is called the blind spot area. The reason is the construction of the vehicle, the bigger the vehicle, the bigger the blind spot. Based on the literature, fuzzy logic with the Sugeno method can provide decisions/reasoning that are easier for humans to understand. In this research, an object detection system was developed in the blind spot dump truck area using Fuzzy Logic with the Sugeno method. This object detection system uses 5 ultrasonic sensors as a detection device (input), as well as a buzzer and LED as a warning (output). Based on the results of research that has been tested using white-box testing, each existing test case has been tested and validated. With the closest testing distance of 2 cm, and the farthest distance from detection is 300 cm, and testing on black-box testing at the user level is under the function with a value of 100%.
Cloud Information Systems

With the development of information technologiesInformation Technology (IT), the needs such as transferring and storing dataDatum, and providing quick access to the dataDatum have arisen. Cloud service providers have emerged on these needs. BusinessesBusiness can stream and store dataDatum, and access it at any time over a fixed portal thanks to cloud service providers which have advantages such as low storageStorage costs, working with cross-platforms, fast dataDatum flow and access facilities at any time. In this context, considering the importance of dataDatum in the effective management of commercialCommercial organizationsOrganizations, these advantages of cloud computing systemsComputing systems offer great opportunities to businessesBusiness . From this point of view, it can be said that an analytical evaluation is of great importance in the selection of cloud service providers. Within this scope, in this study, the criteria such as performancePerformance, satisfaction, dataDatum transfer rate, suitability and networkNetwork latency, etc.) that businessesBusiness should consider rentingRenting the most suitable cloud service provider were examined and cloud service providers were compared under these criteria by suitable multi-criteria decisionDecision-making algorithmsAlgorithms.
Visual Acuity Screening for early detection on Students Using Twin Cards Optotype

Myopia is seen as a benign condition which is a public health problem and must be given attention, concerning visual problems, quality of life, and economic consequences. Nearly half of the world's blindness has been caused by uncorrected refraction, with myopia occupying a high percentage of the group, meaning myopia is the beginning of blindness. Uncorrected disturbances of visual acuity should be screened This study aims to develop a method of screening for myopia in school children, resulting in twin card optotypes. The study was carried out by diagnostic tests to analyze the sensitivity and specificity of twin card optotypes in screening for visual impairment in schoolchildren using primary data from the visual test. The results of this study are twin card optotypes which can easily be used in almost any condition, without requiring access to electricity, not causing variables bullies in a row of questions on the same letter size. Faster time because it does not cause repeated questions one paper one object. Installation does not require encoding on the wall, avoid memorization and avoid factors from concerns about clinical validity and application accuracy. Conclusion Sensitivity of sharp vision screening method Optotip twin card when compared with snoten optotip vision sharpening method which is 83.33% while specificity is 71.43 %. Keywords: Screening, myopia, twin card optotypes
Notes about symmetric m-adic complexity of generalized cyclotomic sequences of order two with period pq

Abstract In this paper, we consider binary generalized cyclotomic sequences with period pq , where p and q are two distinct odd primes. These sequences derive from generalized cyclotomic classes of order two modulo pq . We investigate the generalized binary cyclotomic sequences as the sequences over the ring of integers modulo m for a positive integer m and study m -adic complexity of sequences. We show that they have high symmetric m -adic complexity. Our results generalize well-known statements about 2-adic complexity of these sequences.
Evaluating Selective Pressures Driving Clades Differentiation of the &lt;i&gt;Faecalibacterium prausnitzii&lt;/i&gt; Complex

Faecalibacterium prausnitzii is one of the most prevalent and abundant polyphyletic health- promoting components of the human gut microbiome with a propensity for dysbiotic losses. To better understand its biology in the human gut, we specifically explored the divergence pressures acting on F. prausnitzii clades on a global scale. Five F. prausnitzii clades were de novo identified from 55 publicly available genomes and 92 high-quality metagenome assembled genomes. Divergence rate indices were constructed and validated to compare the divergence rates among the different clades and between each of the diverging genes. For each clade we identified specific patterns of diverging functionalities, aimed at coping with environmental changes, increasing the inter-host dispersion capacity, or exploiting a copiotrophic gut environment. Finally, we speculate that these differences may explain, at least in part, the observed differences in the overall divergence rates of F. prausnitzii clades in human populations.
Using Self-developed Mobile APP and Arduino to Provide Integrated Multimedia for Lower Graders’ English Vocabulary Learning

This study investigated the effects of using self-developed mobile APP and Arduino in English vocabulary learning, to find out the impact on students’ vocabulary learning ability, to understand students’ perception of such learning patterns, and to evaluate the feasibility of lower graders using APP in English vocabulary learning. Arduino was adopted to be associated with APP to provide integrated multimedia support. The study adopted Single Subject Research Design. Five first graders from New Taipei City Taiwan were selected and the experiment was conducted for 10 times in 8 weeks. Data were collected through testing, observation, interviews and questionnaires. The results showed that self-developed English learning APP has a positive impact on students’ vocabulary learning ability. Students gave positive response and held positive attitude toward English learning through APP. Students were willing to learn with English learning APP and enjoy learning. The integration of English learning and APP and Arduino was addressed and hoped to broaden the spectrum of English learning design in mobile devices.
Beyond Functional Speech Synthesis

As synthetic voices enter the mass market, there is an increasing need for voice personalisation, that is, a voice for the text-to-speech system that not only conveys information but also exudes a persona much like the human voice. We begin this chapter with a historical overview of the field starting with model-based approaches, to concatenative systems and finally to contemporary implementations of parametric synthesis. We then examine how the confluence of increased computational speed at reduced costs, the availability of large data sets, and advances in machine learning and artificial intelligence enable a whole new approach to speech synthesis, including the ability to create high-quality personalised voices. We then examine the role of crowdsourcing in developing a scalable method for voice customisation and adaptation. We discuss the benefits and challenges of acquiring recordings of novice voice talent of all ages from around the world and using these recordings for voice building. We conclude by discussing the impact of personalised voices and implications for future work.
Implementation of Pathfinding Algorithm in Sundanese Land History Educational Game

Sunda has a long history recorded in ancient manuscripts containing Carita Parahyangan written about the kingdom's story in java. Development of technology, made delivery of cultural and historical values can be conveyed through Games. Artificial Intelligence makes Non- Playable Character (NPC) move from one position to the target position in the Games application by applying the Pathfinding Algorithm. The application of Artificial Intelligence is to provide a realistic playing experience for users. This study aims to Apply the Pathfinding A * algorithm to games application History of Sundanese Land. With the construction of this game, it is hoped that it can become a medium for Sundanese history education for people. This game was built using the Digital Games Based Learning - Instructional Design methodology and tested using Black Box Testing and scenario testing against the Pathfinding A* Algorithm applied. The application of the A* pathfinding algorithm was successfully carried out on enemy NPCs indicated by the NPC can move to the player's character position through the fastest path. The game that was built gave a positive effect with a value of 9.08 through Game Experience Questionnaire.
Contact tracing, proximity safety, data correlation and visualization for global corporations

As employees are preparing to return to work globally, entering large-scale corporate and industrial environments, ensuring their safety is of paramount concern for their employers. The creation and adherence to new safety protocols are one way that corporations will attempt to achieve this. As the precautionary requirements are scaled to a global multinational corporation, the challenge increases. Traditional physical and visual methods to ensure social distancing are limited, inflexible, and may not deliver a comprehensive and agile solution. A complete end to end contact tracing and social distancing solution that can scale to meet the need of large organisations is required. This solution should provide employees with personal cues when the appropriate physical distancing guidelines are not available. The solution developed during this research combines multiple commercially available wireless and networking infrastructure technologies to provide robust retrospective contact tracing capabilities. The solution will accommodate the need for rapid data collation and visualization from locations worldwide and provides a single, secure management view to enable public health teams to react to potential incidents rapidly. This approach supports a rapid deployment on a global scale across live diverse corporate and manufacturing environments. It is installed in a manner that is complementary to existing technology without causing business impact or interruption. Data collected during the pilot suggest that the end to end solution meets the technical requirements to deliver a comprehensive proximity altering and contact tracing solution for large scale global enterprises. The proposed solution is easy to install, manage and is cost efficient.
The Effects of Steering Locomotion on User Preference and Accuracy in Virtual Environments

Abstract Locomotion in virtual environments presents challenges due to the discrepancy between the virtual and the real-world space. Teleportation has been suggested for rapid transit and low cybersickness. However, users often find the method disorienting and difficult over short distances. This is problematic in many gaming scenarios where moderate distances are common. We examined three methods of self-directed, steering locomotion for short to mid-range distances. The methods were pointing, head, and semi-decoupled head and controller. The decoupled method was to explore if game console navigation would be preferred due to familiarity. The experiment focused on user preference and accuracy and had 19 participants. We anticipated that more intuitive methods would be preferred. The pointing method had the greatest impact on accuracy. History of motion sickness susceptibility and prior use of video games did not affect preference with participants favoring the pointing method twice as often over the head method and with none preferring the semi-decoupled method. The pointing method also had lower average illness scores, although not statistically significant. The results suggest that pointing provides an accurate method of locomotion while also being a lower cybersickness option for steering navigation.
Recognition of English Capital Alphabet in Air writing Using Convolutional Neural Network and Intel RealSense D435 Depth Camera

The study focused on the recognition of the English Capital Alphabet written in free air using Convolutional Neural Network (CNN). The previous study conducted by Balesteros et al. (2018) used a slope orientation sequence matching algorithm. Wherein, the slope defined by the plot of continuous stream of points is used to represent Alphanumeric Characters. Also, in this study the proponents achieved the system’s reliability in recognizing alphanumeric characters in average is 97%. However, some alphanumeric character slope orientation sequence is nearly identical. Letters U and V, as well as letters D and P, have identical slope orientation sequences resulting in misclassification. In order to solve this problem and improve the accuracy of recognizing air-drawn English Capital Alphabet, CNN, a deep learning algorithm is used. CNN is primarily used to search for patterns in an image. As it digs deeper, it recognizes the correct trait on its own until it gets a high-level representation of the predicted outcome. By using CNN, the system’s reliability in recognizing English Capital Alphabet characters on average is 99.8%.
Dyn-Asr: Compact, Multilingual Speech Recognition Via Spoken Language And Accent Identification

Running automatic speech recognition (ASR) on edge devices is non-trivial due to resource constraints, especially in scenarios that require supporting multiple languages. We propose a new approach to enable multilingual speech recognition on edge devices. This approach uses both language identification and accent identification to select one of multiple monolingual ASR models on-the-fly, each fine-tuned for a particular accent. Initial results for both recognition performance and resource usage are promising with our approach using less than 1/12th of the memory consumed by other solutions.
Illumination condition model system of exhibition hall design based on computer aided design

Using the dimming control system, it is very convenient to control the lighting brightness of the lighting circuit according to the use requirements of different scenes at different times. It not only realizes the automatic control of lighting, but also creates and beautifies the light environment of the space inside and outside the building, and reduces the energy consumption and prolongs the service life of lamps. In view of this, the lighting condition model system of exhibition hall design based on computer aided design is put forward. Based on the optimization strategy of genetic algorithm, the visual comfort condition satisfied by the quantitative design of green lighting and the quantitative method for calculating the illumination intensity of green lighting are obtained. The system adopts ZigBee wireless network to realize data transmission, wireless monitoring, control and management among nodes of intelligent lighting system. As a bridge between the terminal and the server, the system gateway is responsible for monitoring information communication between ZigBee and Ethernet protocols. The server collects the monitoring information of the storage terminal and provides an interface for accessing and controlling the terminal.
Second-Order Statistical Modeling Method of Depth Features in Image Classification

Second-order statistical modeling method of depth features in image classification is studied in this paper. Through the confrontation training of feature generators and classifiers, this encourages the generator to output the features with category information, which improves the generalization ability of the model to a certain extent. Spatial relationship modeling, as an important means to realize image semantic expression can effectively enhance the performance of visual feature expression, the statistical modeling is then applied to the initial model to obtain the better complexity of the framework. The proposed method will be tested on the public data and also the robustness is tested.
Domain-based semi-supervised learning: exploiting label invariance in unlabeled data from distributed cameras

In several practical supervised learning problems where we have a large amount of data from distributed cameras or sensors, we can use domain knowledge to identify subsets of unlabeled examples with the same (unknown) label. Under this assumption, we propose a straightforward way to exploit label invariance in unlabeled data within a domain-aware semi-supervised learning framework (DSSL). Our approach exploits such invariance to generate higher quality pseudolabels to be used in a consistency loss term.We report experiments and ablation studies on three practical cases on data from real-world fleets of connected vehicles that naturally exhibit the required assumption: an image classification problem, a semantic segmentation task, and a time series classification one. We show that our approach is extremely effective, especially when few labeled samples are available, and can be easily adapted to tasks of different nature.
Pseudo-random sequences of non-maximum length on shift registers with reducible and primitive polynomials

Abstract Inhomogeneous pseudo-random sequences of non-maximal length formed by shift registers with linear feedbacks based on a characteristic polynomial of degree n of the form ϕ( x )=ϕ 1 ( x )ϕ 2 ( x ), where ϕ 1 ( x ) = x m 1 ⊕ 1, and ϕ 2 ( x ) of degree m 2 is primitive ( m 1 = 2 k , k is a positive integer, n = m 1 + m 2 ) are considered. Three schemes that are equivalent in terms of periodic sequence structures were considered. Of the greatest interest are the shift registers connected in an arbitrary way using a modulo-two adder, the feedbacks in which correspond to the multipliers ϕ 1 ( x ) and ϕ 2 ( x ) the polynomials ϕ( x ). In this case, there is a complex process of forming output sequences, which involves both direct and inverse M-sequences. The statement about the singularity of the generated sequences at m 1 = 4 is proved, which is confirmed by their decimation with an index equal to the period of the primitive polynomial.
Computer Vision for Supporting Image Search

Computer vision and multimedia information processing have made extreme progress within the last decade and many tasks can be done with a level of accuracy as if done by humans, or better. This is because we leverage the benefits of huge amounts of data available for training, we have enormous computer processing available and we have seen the evolution of machine learning as a suite of techniques to process data and deliver accurate vision-based systems. What kind of applications do we use this processing for ? We use this in autonomous vehicle navigation or in security applications, searching CCTV for example, and in medical image analysis for healthcare diagnostics. One application which is not widespread is image or video search directly by users. In this paper we present the need for such image finding or re-finding by examining human memory and when it fails, thus motivating the need for a different approach to image search which is outlined, along with the requirements of computer vision to support it.
Impact of Distributed Rate Limiting on Load Distribution in a Latency-sensitive Messaging Service

The cloud's flexibility and promise of seamless auto-scaling notwithstanding, its ability to meet service level objectives (SLOs) typically calls for some form of control in resource usage. This seemingly traditional problem gives rise to new challenges in a cloud setting, and in particular a subtle yet significant trade-off involving load-distribution decisions (the distribution of workload across available cloud resources to optimize performance), and rate limiting (the capping of individual workloads to prevent global over-commitment). This paper investigates that trade-off through the design and implementation of a real-time messaging system motivated by Internet-of- Things (IoT) applications, and demonstrates a solution capable of realizing an effective compromise. The paper's contributions are in both explicating the source of this trade-off, and in demonstrating a possible solution.
Image Repair Methods Based on Deep Residual Networks

In recent years, deep learning has shown significant advantages in image restoration. Compared with the traditional repair method, the image repair method based on deep learning can better solve the problem of image missing blur, but it will also cause the problem of local color difference of the repair image. In this paper, an image repair model based on deep residual network is proposed, which is divided into repair module and mitigation module. The repair module uses part of the convolution network to repair the missing area of image blur, the mitigation module uses the deep residual network to adjust the color difference of the repaired image, and the two modules coordinate with each other to make the image repair effect closer to the real image. The experimental results show that the method proposed in this paper has better effect on image repair.