Deep GUI: Black-box GUI Input Generation with Deep Learning

Information about mental health systems is essential for mental health planning to reduce the burden of neuropsychiatric disorders. Unfortunately, many low- and middle-income countries lack systematic information on their mental health systems. The objectives, scope, structure, and contents of mental health assessment and monitoring instruments commonly used in high-income countries may not be appropriate for use in middle- and low-income countries. The World Health Organization (WHO) has recently developed the WHO Assessment Instrument for Mental Health Systems (WHO-AIMS), a comprehensive assessment tool for mental health systems designed for middle- and low-income countries. WHO-AIMS was developed through an iterative process that included input from in-country and international experts on the clarity, content, validity, and feasibility of the instrument, as well as a pilot trial. The resulting instrument, WHO-AIMS 2.2, consists of six domains: policy and legislative framework, mental health services, mental health in primary care, human resources, public information and links with other sectors, and monitoring and research. These domains address the ten recommendations of the World Health Report 2001 through 28 facets and 155 items. All six domains need to be assessed to form a basic, yet broad, picture of a mental health system, with a focus on health sector activities. WHO-AIMS provides essential information for mental health policy and service delivery. Countries will be able to develop information-based mental health policy and plans with clear baseline information and targets. Moreover, they will be able to monitor progress in implementing reform policies, providing community services, and involving consumers, families, and other stakeholders in mental health promotion, prevention, care and rehabilitation. This article provides an overview of the rationale, development process, and potential uses and benefits of WHO-AIMS.

IntroductionAyushman Bharat Pradhan Mantri Jan Aarogya Yojana (AB PM-JAY) has enabled the Government of India to become a strategic purchaser of health care services from private providers. To generate base cost evidence for evidence-based policymaking the Costing of Health Services in India (CHSI) study was commissioned in 2018 for the price setting of health benefit packages. This paper reports the findings of a process evaluation of the cost data collection in the private hospitals.MethodsThe process evaluation of health system costing in private hospitals was an exploratory survey with mixed methods (quantitative and qualitative). We used three approaches–an online survey using a semi-structured questionnaire, in-depth interviews, and a review of monitoring data. The process of data collection was assessed in terms of time taken for different aspects, resources used, level and nature of difficulty encountered, challenges and solutions.ResultsThe mean time taken for data collection in a private hospital was 9.31 (± 1.0) person months including time for obtaining permissions, actual data collection and entry, and addressing queries for data completeness and quality. The longest time was taken to collect data on human resources (30%), while it took the least time for collecting information on building and space (5%). On a scale of 1 (lowest) to 10 (highest) difficulty levels, the data on human resources was the most difficult to collect. This included data on salaries (8), time allocation (5.5) and leaves (5).DiscussionCost data from private hospitals is crucial for mixed health systems. Developing formal mechanisms of cost accounting data and data sharing as pre-requisites for empanelment under a national insurance scheme can significantly ease the process of cost data collection.

The railway signaling system is being converted to the computer system from the exiting electrical equipment, and then the dependency on software is being increased. So far, the software validation has been mainly dependent profess, the these methodology is just depended to software experts as a tester. So it is our situation where not only validation of the document on safety activities being required, but also another automatci testing tool for vital railway signaling software. There were difficult to take advantage at the existing software black-box testing tool like usage of internal memory access of target system. This paper have proposed a new black-box testing tools with enhanced usability and the represents the design and prototype result for proposed tool.

We conducted an electronic, self-administered cross-sectional survey of all ODOs in Canada from November 2021 to January 2022. We targeted key knowledge holders familiar with the data collection processes within each Canadian ODO known to Canadian Blood Services. Categorical item responses are presented as numbers and proportions. We achieved a 100% response rate from 10 Canadian ODOs. Most data were collected by organ donation coordinators. Only 2 of 10 ODOs reported using scripts explaining why sociodemographic data are being collected or incorporated training in cultural sensitivity for any given variable. A lack of cultural sensitivity training was endorsed by 50% of respondents as a barrier to the collection of sociodemographic variables by ODOs, whereas 40% of respondents identified a lack of training in sociodemographic variable collection as a significant barrier. Few programs routinely collect sufficient data to examine health inequities with an intersectional lens. Most data collection occurs midway through the ODO interaction, creating a missed opportunity to better understand differences in social identities of patients who register their intention to donate in advance or who decline the donation. National standardization of equity-relevant data collection definitions and processes of the collection is needed.

Real-time data collection and analysis processes have been designed and an initial capability is demonstrated for the July, 1994 JTMDSN test data. The JTMDSN is a one-year, distributed interactive simulation (DIS) demonstration sponsored by the Defense Modeling and Simulation Office (DMSO). The JTMDSN is built on past TACCSF and RESA DIS accomplishments such as their War Breaker support, to make two more major contributions to the DIS community. First Army, Navy, Air Force, and National Systems interfaced via tactical data links using signal Protocol Data Units (PDUs). Second, the data was collected online from PDUs and reduced to records of key events which could be used to display mission performance and network performance measures in real-time. This paper focuses on this data collection and analysis capability. The JTMDSN DIS Gateway, including the data collection process, was designed and developed by Martin Marietta Corporation. Data analysis processes are being developed by BDM Engineering Services Company. >

The conventional induction motor rotor flux observer based on current model and voltage model are sensitive to parameter uncertainties. In this paper, a non-parametric induction motor rotor flux estimator based on feed-forward neural network is proposed. This estimator is operating without motor parameters and therefore it is independent from parameter uncertainties. The model is trained using Levenberg-Marquardt algorithm offline. All the data collection, training and testing process are fully performed in MATLAB/Simulink environment. A forced iteration of 1,000-epochs is imposed in the training process. There are overall 603,968 datasets are used in this modeling process. This four-input two-output neural network model is capable of providing rotor flux estimation for field-oriented control systems with 3.41e-9 mse and elapsed 28 minutes 49 seconds training time consumption. This proposed model is tested with reference speed step response and parameters uncertainties. The result indicates that the proposed estimator improves voltage model and current model rotor flux observers for parameters uncertainties.

With recent breakthroughs in Deep Learning (DL), DL systems are increasingly deployed in safety-critical fields. Hence, some software testing methods are required to ensure the reliability and safety of DL systems. Since the rules of DL systems are inferred from training data, it is difficult to know the implementation rules about each behavior of DL systems. At the same time, Random Testing (RT) is a popular testing method and the knowledge about software implementation is not needed when we use RT. Therefore, RT is very suitable for the testing of DL systems. And the existing mechanisms for testing DL systems also depend heavily on RT by the labeled test data. In order to increase the effectiveness of RT for DL systems, we design, implement and evaluate the Adaptive Random Testing for DL systems (ARTDL), which is the first Adaptive Random Testing (ART) method to improve the effectiveness of RT for DL systems. ARTDL refers to the idea of ART. That is, fewer test cases are needed to detect failures by selecting the test case with the furthest distance from non-failure-causing test cases. Firstly, we propose the Feature-based Euclidean Distance (FED) as the distance metric that can be used to measure the difference between failure-causing inputs and non-failure-causing inputs. Secondly, we verify the availability of FED by presenting the failure pattern of DL models. Finally, we design ARTDL algorithm to generate the test cases that are more likely to cause failures based on the FED. We implement ARTDL to test top performing DL models in the field of image classification and automatic driving. The results show that, on average, the number of test cases used to find the first bug is reduced by 62.74% through ARTDL, compared with RT.

BackgroundA national study, ‘Costing of healthcare services in India’ (CHSI) aimed at generating reliable healthcare cost estimates for health technology assessment and price-setting is being undertaken in India. CHSI sampled 52 public and 40 private hospitals in 13 states and used a mixed micro-costing approach. This paper aims to outline the process, challenges and critical lessons of cost data collection to feed methodological and quality improvement of data collection.MethodsAn exploratory survey with 3 components–an online semi-structured questionnaire, group discussion and review of monitoring data, was conducted amongst CHSI data collection teams. There were qualitative and quantitative components. Difficulty in obtaining individual data was rated on a Likert scale.ResultsMean time taken to complete cost data collection in one department/speciality was 7.86(±0.51) months, majority of which was spent on data entry and data issues resolution. Data collection was most difficult for determination of equipment usage (mean difficulty score 6.59±0.52), consumables prices (6.09±0.58), equipment price(6.05±0.72), and furniture price(5.64±0.68). Human resources, drugs & consumables contributed to 78% of total cost and 31% of data collection time. However, furniture, overheads and equipment consumed 51% of time contributing only 9% of total cost. Seeking multiple permissions, absence of electronic records, multiple sources of data were key challenges causing delays.ConclusionsMicro-costing is time and resource intensive. Addressing key issues prior to data collection would ease the process of data collection, improve quality of estimates and aid priority setting. Electronic health records and availability of national cost data base would facilitate conducting costing studies.

Peas, as a globally significant legume crop, play a crucial role in food production and human nutrition. However, field pests and diseases, drought, high temperature and nutrient deficiency and other stresses seriously affect the yield and quality of peas. Timely and efficient stress monitoring is of great significance for ensuring pea production. This study reviews the advantages of unmanned aerial vehicle (UAV) remote sensing platforms in farmland stress monitoring, including high-throughput phenotypic acquisition and the roles of multispectral, RGB, and thermal infrared sensors. It also analyzes the applicability of commonly used deep learning models (CNN, RNN, Transformer) in crop stress detection. And the application progress of image classification, object detection, and semantic segmentation technologies in identifying crop stress types, a technical framework combining unmanned aerial vehicles and deep learning was constructed. The data collection and annotation process, data preprocessing and enhancement methods, as well as the pipeline for fusing multi-source images with deep learning models were expounded. The identification, degree classification and spatial distribution visualization of physiological stress (drought, nutrient deficiency, high temperature) and biological stress (diseases, pests) were mainly discussed. In the case of field stress detection of peas, the recognition performance of the deep learning model and its application value in field management decisions were verified. The combination of unmanned aerial vehicle (UAV) remote sensing and deep learning technology can achieve precise detection of field stress in peas. This study aims to provide strong support for precision agricultural management.

Reading the Qur'an is crucial to prevent humanity from going astray in life. To comprehend the content and meaning of any text, one must first read it; this holds true for the Qur'an as well. Motivation is a key factor that encourages Muslims to regularly read the Qur'an. In the digital era, the Qur'an Android app has become a significant tool for encouraging teenagers to engage in Qur'anic reading. This study aims to describe: (1) The impact of using the Qur'an Android app on the motivation to read the Qur'an among teenagers at SMP IT Al-Falah Cilawu. (2) The level of motivation for reading the Qur'an among teenagers at SMP IT Al-Falah Cilawu. (3) The effect of the Qur'an Android app on the reading motivation of teenagers at SMP IT Al-Falah. Consequently, this study seeks to understand the relationship between the use of the Qur'an Android app (Variable X) and the motivation to read the Qur'an among teenagers (Variable Y). The research employs an inferential quantitative method, gathering data and information about the phenomena under study. Data collection techniques include questionnaires distributed to 27 respondents, observations, and document studies. Data analysis is conducted using statistical data analysis methods. The statistical test results indicate that the use of the Qur'an Android app influences reading motivation among students at SMP IT Al-Falah Muhammadiyah Cilawu Garut by 33.55%. The hypothesis test results show that the impact of using the Qur'an Android app on the reading motivation of 9th-grade students at SMP IT Al-Falah Cilawu is 43.5%, while the remaining 56.45% is influenced by other factors not covered in this study. Keywords: Qur'an Android App, Reading Motivation, Impact on Students at SMP IT Al-Falah Muhammadiyah Cilawu Garut

With the aim of identifying possible mechanical faults in unmanned aerial vehicle (UAV) rotors during operation, this paper proposes a method based on interval sampling reconstruction of vibration signals and one-dimensional convolutional neural network (1D-CNN) deep learning. Firstly, experiments were designed to collect the vibration acceleration signals of a UAV working at high speed under three states (normal, rotor damage by varying degrees, and rotor crack by different degrees). Then, considering the powerful feature extraction and complex data analysis abilities of 1D-CNN, an effective deep learning model for fault identification is established utilizing 1D-CNN. During analysis, it is found that the recognition rate for minor faults is not ideal, with all weak states being identified as normal, reducing the overall identification accuracy, when using conventional sequential sampling to construct learning sample sets. To this end, in order to make the sample data cover the whole process of data collection as much as possible, a learning sample processing method based on interval sampling reconstruction of the vibration signal is proposed. And it is also verified that the reconstructed sample set can easily reflect the global information of mechanical operation. Finally, according to the comparison of analysis results, the recognition rate of the deep learning model for different degrees of faults is greatly improved, and minor faults could also be accurately identified through this method. The results show that the 1D-CNN deep learning model could diagnose and identify UAV rotor damage faults accurately, by incorporating the proposed method of interval sampling reconstruction.

Intersection markings play a vital role in providing road users with guidance and information. The conditions of intersection markings will be gradually degrading due to vehicular traffic, rain, and/or snowplowing. Degraded markings can confuse drivers, leading to increased risk of traffic crashes. Timely obtaining high-quality information of intersection markings lays a foundation for making informed decisions in safety management and maintenance prioritization. However, current labor-intensive and high-cost data collection practices make it very challenging to gather intersection data on a large scale. This paper develops an automated system to intelligently detect intersection markings and to assess their degradation conditions with existing roadway Geographic information systems (GIS) data and aerial images. The system harnesses emerging artificial intelligence (AI) techniques such as deep learning and multi-task learning to enhance its robustness, accuracy, and computational efficiency. AI models were developed to detect lane-use arrows (85% mean average precision) and crosswalks (89% mean average precision) and to assess the degradation conditions of markings (91% overall accuracy for lane-use arrows and 83% for crosswalks). Data acquisition and computer vision modules developed were integrated and a graphical user interface (GUI) was built for the system. The proposed system can fully automate the processes of marking data collection and condition assessment on a large scale with almost zero cost and short processing time. The developed system has great potential to propel urban science forward by providing fundamental urban infrastructure data for analysis and decision-making across various critical areas such as data-driven safety management and prioritization of infrastructure maintenance.

In recent years, deep reinforcement learning has combined the advantages of reinforcement learning and deep learning, and has made great progress in decision-making tasks. However, the training of deep reinforcement learning requires frequent interactions between the agent and the environment and repeated experiments. Adversaries have chances to poison the sample data collected by the agent by attacking the experimental environment in the training process, thereby bringing security risks and serious consequences to the training process of reinforcement learning. This work is committed to addressing the security risks in the field of deep reinforcement learning. However, this work improves the algorithm from the perspective of sample data filtering, and improves the security performance of deep reinforcement learning algorithm. There are two contributions in this work: one is to defend against adversarial attacks against deep reinforcement learning through cluster analysis and sample value evaluation; the other is to propose a deep reinforcement learning algorithm based on sample value evaluation on the basis of deterministic strategy gradient algorithm. The algorithm uses the clustering method to classify the sample pool, and measures the contribution value and security risk of the sample to the model training through the sample value evaluation. The classic game experiments show that the proposed algorithm is safe and effective. It reduces the threat of the agent falling into the adversarial sample attack and improves the training performance of deep reinforcement learning.

Digital technology can be used to solve problems that arise from business activity. One company that has not optimally used digital technology is PT Tiga Tapak Mutiara, which is engaged in manufacturing shoe labels. In its business process, each related division collects data, and the data collection results will be reported to the manager to unify work activities. The production data collection process has not been carried out optimally by the relevant divisions because the conventional recording is still carried out using a spreadsheet application which can slow down the reporting process to be monitored by the leadership. As a result, a slow process can impact not completing production on time. In addition to the problem of data collection, another problem is that the completion of orders received earlier is not compared to the orders received last. The aim and contribution of this research are to integrate business processes to overcome these problems in data collection, monitoring, and order completion activities. It is necessary to design a web-based monitoring information system with the Rapid Application Development (RAD) method and the application of the First In, First Out (FIFO) method so that the ordering process can run according to the time of order. The result of this research is a web-based information system that is useful for integrating business processes to monitor and optimize production activities to be completed on time. This web-based information system has also passed the functionality test using black box testing and is following the company's needs.

According to Government Computer News (GCN) web attacks have been marked as all- time high this year. GCN says that some of the leading security software like SOPHOS detected about 15,000 newly infected web pages daily in initial three months of 2008 [13]. This has lead to the need of efficient software to make web applications robust and sustainable to these attacks. While finding information on different types of attacks, I found that SQL injection and cross site scripting are the most famous among attackers. These attacks are used extensively since, they can be performed using different techniques and it is difficult to make a web application completely immune to these attacks. There are myriad detection tools available which help to detect vulnerabilities in web applications. These tools are mainly categorized as white-box and black-box testing tools. In this writing project, we aim to develop a detection tool which would be efficient and helpful for the users to pinpoint possible vulnerabilities in his/her PHP scripts. We propose a technique to integrate the aforementioned categories of tools under one framework to achieve better detection against possible vulnerabilities. Our system focuses on giving the developer a simple and concise tool which would help him/her to correct possible loopholes in the PHP code snippets.