Computer Experts Research Articles

Android-based Learning Media Development for Computer and Basic Networking at State Vocational High School 1 Bandung Tulungagung

This development aims to design and develop android-based learning media for Computer and Networking subjects in Class X of Computer and Network Engineering of State Vocational High School 1 Bandung Tulungagung. The development method applied in this development uses the ADDIE development model. This model has five steps, namely (1) analysis, (2) design, (3) development, (4) implementation, (5) evaluation. This android-based learning media was then tested on experts and 30 students of class XI of the Computer and Network Engineering Expertise Program at State Vocational High School 1 Bandung Tulungagung. The product produced in this development is an android-based learning media. After analyzing the data, the proportion of learning media from expert I was 82.50 percent, expert II was 83.33 percent, small group trial was 86.50 percent, field trial was 88.58 percent. The overall average percentage of expert I, expert II, small group trial, and field trial was 86.38 percent

Paths to computational fluency for natural resource educators, researchers, and managers

AbstractNatural resource management and supporting research teams need computational fluency in the data and model‐rich 21st century. Computational fluency describes the ability of practitioners and scientists to conduct research and represent natural systems within the computer's environment. Advancement in information synthesis for natural resource management requires more sophisticated computational approaches, as well as reproducible, reusable, extensible, and transferable methods. Despite this importance, many new and current natural resource practitioners lack computational fluency and no common set of recommended resources and practices exist for learning these skills. Broadly, attaining computational fluency entails moving beyond the simple use of computers to applying sound computational principles and methods and including computational experts (such as computer scientists) on research teams. Our path for computational fluency includes using open‐source tools when possible; reproducible data management, statistics, and modeling; understanding and applying the benefits of basic computer programming to carry out more complex procedures; tracking code with version control; working in controlled computer environments; and using advanced computing resources.Considerations for Resource Managers Natural resource management increasingly uses computer‐generated results to inform and guide decision making. Open science requires that these data and software be reproducible. In turn, open science promotes computational fluency among natural resource managers, researchers, and educators. Based upon our experiences and perspectives working to support natural resource professionals, many natural resource managers would like to attain computationally fluency, yet lack formal training. We provide a path to computational fluency that emphasizes: using open‐source tools when possible; reproducible data management, statistics, and modeling; understanding and applying the basic computer programming philosophy to carry out both simple and more complex procedures; tracking code with version control; working in controlled computer environments; and using advanced computing resources. Natural resource managers, educators, and researchers can use their computationally fluency skills to promote open science in their own work.

Linnea

The translation of linear algebra computations into efficient sequences of library calls is a non-trivial task that requires expertise in both linear algebra and high-performance computing. Almost all high-level languages and libraries for matrix computations (e.g., Matlab, Eigen) internally use optimized kernels such as those provided by BLAS and LAPACK; however, their translation algorithms are often too simplistic and thus lead to a suboptimal use of said kernels, resulting in significant performance losses. To combine the productivity offered by high-level languages, and the performance of low-level kernels, we are developing Linnea, a code generator for linear algebra problems. As input, Linnea takes a high-level description of a linear algebra problem; as output, it returns an efficient sequence of calls to high-performance kernels. Linnea uses a custom best-first search algorithm to find a first solution in less than a second, and increasingly better solutions when given more time. In 125 test problems, the code generated by Linnea almost always outperforms Matlab, Julia, Eigen, and Armadillo, with speedups up to and exceeding 10×.

Convolutional Neural Networks for the evaluation of cancer in Barrett's esophagus: Explainable AI to lighten up the black-box

Even though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their level of accountability and transparency must be provided in such evaluations. The reliability related to machine learning predictions must be explained and interpreted, especially if diagnosis support is addressed. For this task, the black-box nature of deep learning techniques must be lightened up to transfer its promising results into clinical practice. Hence, we aim to investigate the use of explainable artificial intelligence techniques to quantitatively highlight discriminative regions during the classification of early-cancerous tissues in Barrett's esophagus-diagnosed patients. Four Convolutional Neural Network models (AlexNet, SqueezeNet, ResNet50, and VGG16) were analyzed using five different interpretation techniques (saliency, guided backpropagation, integrated gradients, input × gradients, and DeepLIFT) to compare their agreement with experts' previous annotations of cancerous tissue. We could show that saliency attributes match best with the manual experts' delineations. Moreover, there is moderate to high correlation between the sensitivity of a model and the human-and-computer agreement. The results also lightened that the higher the model's sensitivity, the stronger the correlation of human and computational segmentation agreement. We observed a relevant relation between computational learning and experts' insights, demonstrating how human knowledge may influence the correct computational learning.

Criminalistics analysis of cyber tools for committing crimes

The article is devoted to the problems of the information and communication technologies which are used as cyber tools for committing crimes. Due to the coronavirus and the widespread lockdown, crimes have also moved to online space. The empirical base of cybercrime is analyzed taking into consideration the latest trends caused by the ongoing pandemic. Mostly this category of crimes is latent and quite difficult to investigate. Especially due to the lack of knowledge in the field of informatics, which is constantly being transformed (changed), by investigators and other participants of the pre-trial investigation. Modern scientific and technical tools are needed to detect, collect and fixed electronic traces of cybercrimes. It should be enhance the technical capabilities of forensic laboratories that specialize in the investigation of cybercrimes related to the use of information technology. But there is a problem because of lack of financial support and needness quite expensive equipment for conducting new computer expertise. This requires significant additional funding, which is not fully provided to the police station by the government. Cybercrime is a transnational crime that has no borders. Criminals from different countries can unite and detect such complicity in a timely manner is quite difficult. So it is necessary international cooperation with FBI, Interpol and other organizations, law-enforcement bodies. During the investigation it should be reconstruct the stages of the hacker's growth and return to his the very beginning, when he was not a such professional, but only gaining experience. The complication of identifying criminals is also that they use traditional tools such as Wi-Fi routers and non-traditional ones such as "boto-farms" which are used as cybertools for committing the crime.

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Effect of Multimodal Instructional Approach on Students’ Academic Performance in The Concept of Biological Classification

Purpose: This study investigated the effect of Multimodal Instructional Approach on students’ academic performance in the concept of Biological Classification at Navrongo Senior High School, generally, and on gender basis.
 Methodology: The study adopted quasi-experimental research design. The sample comprised of 100 Navrongo Senior High School Form Two Gold Track and Green Track science students. Students from the Gold Track and Green Track were designated experimental group and control group respectively. Each group was made up of 25 males and 25 females. Pretest was administered to all the participants. The experimental group was taught using Multimodal Instructional Approach. The control group was taught using Discussion as the teaching method. A posttest was administered to both groups to determine the effectiveness of the treatment. The reliability of the test items was determined using test-retest reliability coefficient. The test-retest reliability coefficients of the instruments were found to be 0.73 and 0.78 for the pretest and posttest respectively. The data obtained were analysed using independent sample t-test.
 Results: The pretest test scores revealed that the students were homogeneous in terms of performance. Posttest results of students in the experimental group were significantly higher than the control group. There was no significant difference in the performance of males and females. The results of the posttest revealed that the use of MIA in teaching biological classification was more effective than discussion method.
 Unique contribution to theory, practice and policy: This study recommended that Biology teachers in NSHS should be encouraged to teach Biological Classification using MIA in order to improve the academic performance of both male and female students. School authority should invite educational technologists, instructional materials technicians and computer experts to help science teachers of NSHS on how to incorporate ICT in teaching science subjects.

DeepFrag: An Open-Source Browser App for Deep-Learning Lead Optimization.

Lead optimization, a critical step in early stage drug discovery, involves making chemical modifications to a small-molecule ligand to improve properties such as binding affinity. We recently developed DeepFrag, a deep-learning model capable of recommending such modifications. Though a powerful hypothesis-generating tool, DeepFrag is currently implemented in Python and so requires a certain degree of computational expertise. To encourage broader adoption, we have created the DeepFrag browser app, which provides a user-friendly graphical user interface that runs the DeepFrag model in users’ web browsers. The browser app does not require users to upload their molecular structures to a third-party server, nor does it require the separate installation of any third-party software. We are hopeful that the app will be a useful tool for both researchers and students. It can be accessed free of charge, without registration, at http://durrantlab.com/deepfrag. The source code is also available at http://git.durrantlab.com/jdurrant/deepfrag-app, released under the terms of the open-source Apache License, Version 2.0.

Rationality over fashion and hype in drug design

The current hype associated with machine learning and artificial intelligence often confuses scientists and students and may lead to uncritical or inappropriate applications of computational approaches. Even the field of computer-aided drug design (CADD) is not an exception. The situation is ambivalent. On one hand, more scientists are becoming aware of the benefits of learning from available data and are beginning to derive predictive models before designing experiments. However, on the other hand, easy accessibility of in silico tools comes at the risk of using them as “black boxes” without sufficient expert knowledge, leading to widespread misconceptions and problems. For example, results of computations may be taken at face value as “nothing but the truth” and data visualization may be used only to generate “pretty and colorful pictures”. Computational experts might come to the rescue and help to re-direct such efforts, for example, by guiding interested novices to conduct meaningful data analysis, make scientifically sound predictions, and communicate the findings in a rigorous manner. However, this is not always ensured. This contribution aims to encourage investigators entering the CADD arena to obtain adequate computational training, communicate or collaborate with experts, and become aware of the fundamentals of computational methods and their given limitations, beyond the hype. By its very nature, this Opinion is partly subjective and we do not attempt to provide a comprehensive guide to the best practices of CADD; instead, we wish to stimulate an open discussion within the scientific community and advocate rational rather than fashion-driven use of computational methods. We take advantage of the open peer-review culture of F1000Research such that reviewers and interested readers may engage in this discussion and obtain credits for their candid personal views and comments. We hope that this open discussion forum will contribute to shaping the future practice of CADD.

Linear matrix inequalities‐based adaptive time‐delayed sliding mode control for ships considering ship–bank interaction effect and shallow water effect

AbstractTo deal with the uncertain system models with less computational cost, expertise knowledge and parameter tuning, a linear matrix inequalities (LMI)‐based adaptive time‐delayed sliding mode control algorithm is proposed. It is used for ships berthing or sailing near the bank or the pier of a bridge considering ship–bank interaction effect and shallow water effect. The closed‐loop system is proved to be uniform ultimate bounded based on the Lyapunov–Razumikhin function in terms of LMI. The switching function is designed to make the system robust to uncertainties and external disturbances and avoid chattering. The trajectory of the system space is contractive and asymptotically tends to the desired position and attitude. The influence of different control parameters on the control effect are compared and analyzed. The results show that when the control coefficients are small, the overshoot of the system is small. The adjustment time of the system is long. When the control coefficients are large, the overshoot of the system is obvious. The adjustment time of the system is shortened. Influence analysis of different input time delays show that when the input delay is long, it takes longer time for the system to reach its desired position with higher overshoot. When the input delay is short, it takes shorter time for the system to reach its desired position with lower overshoot.

Rationality over fashion and hype in drug design.

The current hype associated with machine learning and artificial intelligence often confuses scientists and students and may lead to uncritical or inappropriate applications of computational approaches. Even the field of computer-aided drug design (CADD) is not an exception. The situation is ambivalent. On one hand, more scientists are becoming aware of the benefits of learning from available data and are beginning to derive predictive models before designing experiments. However, on the other hand, easy accessibility of in silico tools comes at the risk of using them as "black boxes" without sufficient expert knowledge, leading to widespread misconceptions and problems. For example, results of computations may be taken at face value as "nothing but the truth" and data visualization may be used only to generate "pretty and colorful pictures". Computational experts might come to the rescue and help to re-direct such efforts, for example, by guiding interested novices to conduct meaningful data analysis, make scientifically sound predictions, and communicate the findings in a rigorous manner. However, this is not always ensured. This contribution aims to encourage investigators entering the CADD arena to obtain adequate computational training, communicate or collaborate with experts, and become aware of the fundamentals of computational methods and their given limitations, beyond the hype. By its very nature, this Opinion is partly subjective and we do not attempt to provide a comprehensive guide to the best practices of CADD; instead, we wish to stimulate an open discussion within the scientific community and advocate rational rather than fashion-driven use of computational methods. We take advantage of the open peer-review culture of F1000Research such that reviewers and interested readers may engage in this discussion and obtain credits for their candid personal views and comments. We hope that this open discussion forum will contribute to shaping the future practice of CADD.

Artificial Intelligence and Novel Sensing Technologies for Assessing Downy Mildew in Grapevine

Plant diseases and pests cause a large loss of world agricultural production. Downy mildew is a major disease in grapevine. Conventional techniques for plant diseases evaluations are time-consuming and require expert personnel. This work investigates novel sensing technologies and artificial intelligence applications for assessing downy mildew in grapevine under laboratory conditions. In our methodology, machine vision is applied to assess downy mildew sporulation, while hyperspectral imaging is used to explore its potential capability towards early detection of this disease. Image analysis applied to RGB leaf disc images is used to estimate downy mildew (Plamopara viticola) severity in grapevine (Vitis vinifera L. cv Tempranillo). A determination coefficient (R2) of 0.76 ** and a root mean square error (RMSE) of 20.53% are observed in the correlation between downy mildew severity by computer vision and expert’s visual assessment. Furthermore, an accuracy of 81% is achieved to detect downy mildew early using hyperspectral images. These results indicate that non-invasive sensing technologies and computer vision can be applied for assessing and quantify sporulation of downy mildew in grapevine leaves. The severity of this key disease is evaluated in grapevine under laboratory conditions. In conclusion, computer vision, hyperspectral imaging and machine learning could be applied for important disease detection in grapevine.

Atmospheric vertical velocity - a crucial component in understanding proximal deposition of volcanic ash

The simulation of volcanic ash transport and deposition (VATD) for distances within a few kilometres from the vent (proximal region) is challenging owing to a combination of unresolved volcanogenic effects and the impact of the volcano's orography. Due to the urgency of calculations or sometimes lack of access to computational resources or expertise, atmospheric vertical velocity (w) is often underestimated in VATD modelling. The error associated with this underestimation has, however, never been properly quantified. Here, we use a weak vulcanian eruption that occurred at Sakurajima volcano on 1/10/2017 as a first step to addressing this limitation. We combine deposit characteristics observed by disdrometer measurements with high-resolution atmospheric and VATD modelling to validate and illustrate the differences in modelled trajectories when w is either ignored or accounted for. The Weather Research and Forecasting (WRF) model is used to model the orogenic effects down to a resolution of 50 m. Eulerian and Lagrangian VATD models (FALL3D and LagTrack, a newly-developed Matlab code, respectively) are used to describe the particle trajectories. Results confirm the importance of w in the case of low-altitude eruptions for capturing the complex, near-vent trajectory of ash particles: when neglected fall velocities were seen to differ up to 2–5 m s−1 depending on the particle size. Although the impact of w is most notable within 10 km from the vent, the forced sedimentation of low terminal velocity particles can have a significant secondary effect at larger distances.

SPEAQeasy: a scalable pipeline for expression analysis and quantification for R/bioconductor-powered RNA-seq analyses

BackgroundRNA sequencing (RNA-seq) is a common and widespread biological assay, and an increasing amount of data is generated with it. In practice, there are a large number of individual steps a researcher must perform before raw RNA-seq reads yield directly valuable information, such as differential gene expression data. Existing software tools are typically specialized, only performing one step–such as alignment of reads to a reference genome–of a larger workflow. The demand for a more comprehensive and reproducible workflow has led to the production of a number of publicly available RNA-seq pipelines. However, we have found that most require computational expertise to set up or share among several users, are not actively maintained, or lack features we have found to be important in our own analyses.ResultsIn response to these concerns, we have developed a Scalable Pipeline for Expression Analysis and Quantification (SPEAQeasy), which is easy to install and share, and provides a bridge towards R/Bioconductor downstream analysis solutions. SPEAQeasy is portable across computational frameworks (SGE, SLURM, local, docker integration) and different configuration files are provided (http://research.libd.org/SPEAQeasy/).ConclusionsSPEAQeasy is user-friendly and lowers the computational-domain entry barrier for biologists and clinicians to RNA-seq data processing as the main input file is a table with sample names and their corresponding FASTQ files. The goal is to provide a flexible pipeline that is immediately usable by researchers, regardless of their technical background or computing environment.

Open-source deep-learning software for bioimage segmentation.

Microscopy images are rich in information about the dynamic relationships among biological structures. However, extracting this complex information can be challenging, especially when biological structures are closely packed, distinguished by texture rather than intensity, and/or low intensity relative to the background. By learning from large amounts of annotated data, deep learning can accomplish several previously intractable bioimage analysis tasks. Until the past few years, however, most deep-learning workflows required significant computational expertise to be applied. Here, we survey several new open-source software tools that aim to make deep-learning–based image segmentation accessible to biologists with limited computational experience. These tools take many different forms, such as web apps, plug-ins for existing imaging analysis software, and preconfigured interactive notebooks and pipelines. In addition to surveying these tools, we overview several challenges that remain in the field. We hope to expand awareness of the powerful deep-learning tools available to biologists for image analysis.

Evaluation of forensic computer and technical expertise in criminal proceedings

The purpose of the article is to clarify the place and role of the expert report based on the results of forensic computer and technical expertise as a source of evidence in criminal proceedings. The subject of the study: The subject of research is forensic computer and technical expertise as a source of evidence in criminal proceedings. Methodology: The method of system analysis, formal and logical, system and structural methods, the methods of modeling and forecasting were used in the course of the research were used in the course of the study. The results of the study: According to the results of the study, the authors conclude that forensic computer and technical expertise is the main procedural form of using special knowledge in the area of computer technology, and its results may be the most important part of the evidence base in the specific criminal proceedings. Practical consequences: It is concluded that the use of the expert report based on the results of forensic computer and technical expertise in criminal proceedings is its application by the subjects of evidence during the qualification of criminal offense to establish facts and circumstances relevant to criminal proceedings and subject to proof, as well as to resolve other tactical tasks. Value / originality: The authors’ definition of assessing expert report based on the results of forensic computer and technical expertise is offered.

Preface

The 1st International Conference on “Applications in Computational Engineering & Sciences “ICONACES 2020, was organized by the School of Mechanical Engineering at Vellore Institute of Technology, Chennai on 30th and 31st Oct 2020. However, due to the COVID 19 pandemic the conference was held Online. The main objective of ICONACES 2020 was intended to bring together the computational experts in Engineering and Sciences in the fields of computational fluid dynamics, finite element methods, boundary element methods, optimisation methods, computational material modelling etc.The conference focused on applications of engineering and sciences in computations domain in Automotive, Bio-engineering, Energy & Environment, Materials, and Automation in which VIT Chennai has an in-house expertise. Further, given the current situation of COVID’19, all major universities and relevant institutes, companies are into developing tools, solutions, products etc to protect mankind from the virus. Hence this conference has included a special track on Computational Aspects related to COVID’19, to provide a platform to share the information and knowledge in this field.IConACES2020 also provided an opportunity to exchange research evidence and innovative ideas and also the issues related to computational engineering and science. This proceeding will include presentations on the latest research areas such as Automotive, Bio-engineering, Energy & Environment, Materials, and Automation.

University Electronic Records Management System for Northwest Samar State University, Calbayog City

Effective records management program is a major element of the governance of any organization. Based from the surveys, the Northwest Samar State University was not fully aware on how to do about the implementation of this act, National Archives of the Philippines Act of 2007 (R.A 9470). The university was still relying from the university code for the records management provisions. This study was undertaken to develop and validate the acceptability of the developed University Electronic Records Management System (UeRMS) for Northwest Samar State University as perceived by the computer experts, school officials, teaching personnel and non- teaching personnel. Thus, the idea and assessment of the respondents towards the design of the system is very critical. Upon validating the developed system, they observed that it was much faster to search records compared to manual process.

Дефиниция «цифровая криминалистика» как начало ложного направления в науке (часть 2)

Purpose. To compare and analyze the trend of appearance of the pseudo-term ‘digital criminology’ in the science of criminology and prove that this is incorrect. Methodology: induction, deduction, synthesis, analysis, formal legal method, comparative legal method, logical method. Conclusions. The evolution of methods of committing crimes and instruments of crime does not always require the evolution of the science of criminology and the emergence of its varieties. You just have to assess the prospects of extrapolative ways and methods of proving the circumstances of the crimes in the era of digitalization of social relations, in terms of the provisions of modern criminal law and only criminal-legal research unit. The definition of ‘digital criminalistics’, which has recently appeared in science, actually misleads the scientific community with its novelty and originality, since such criminalistics in nature does not exist a priori. It is necessary to speak only about forensic or expert research of carriers of digital (electronic or computer) information. In criminalistics, it is logical to develop a new direction-the forensic study of electronic media of digital information and the use of computer expertise in establishing all the circumstances of the crime committed. And this is just a new section of forensic technology, studying a new subject of research for this naki. Here, the traditional forensic methodology should be applied, which will be aimed ‘atlinking’ the electronic carrier of digital information and the digital information itself to a specific crime and to a specific person. Scientific and practical significance. The research is aimed at orienting forensic theory and practice towards the study of specific objects and contributes to the fact that all these studies are further applicable to the practice of crime investigation, and not only for theoretical purposes.

Strategi Manajemen Mengoptimalkan Kinerja dengan Konsep Beyond Budgeting

There are two important elements that create an environment of high freedom for managers and employees, namely the company management culture and the availability of a clear information system as the basis for the management process. The new model of leadership is based on the principle of empowering managers and employees, as well as an adaptable management process. This new leadership principle must be able to unlock all potential managers and employees in running the organization so that they can react appropriately and quickly when facing new opportunities and market risks. To be successful, members of the beyond budgeting project must include control specialists, human resource management, change management, and computer experts. Not all companies are able to provide all the required resources. In traditional budgeting systems, targets are set based on financial figures and are negotiated centrally. In beyond budgeting, targets are based on high key performance indicators, such as the ratio of return on capital, free cash flows or cost to income. Performance appraisals are no longer carried out for the long term (annually), but are carried out in a shorter period of time on a regular basis and are rolling and decentralized. Beyond Budgeting is the idea of abolishing traditional budgeting processes to eventually improve management control over an organization. By abandoning traditional budgeting processes, a company aims to establish a highly decentralized organizational system and adaptive set of management processes.