Reviewerly/exHarmony huggingface dataset:
Convolutional Neural Network Based Mobile Microorganism Detector

Disease detection and prediction are of paramount interest in the area of health-care. The better the diagnosis and prediction, the better ease the treatment would provide and the doctors will be able to cover a larger number of patients in a limited time. Advances in technology have made an impact in the field of medical care from the early detection to assisting in several complicated treatments. However, numerous studies have revealed the lack of health workers in the greater part of the world where patients lack the basic diagnosis and treatment. In addition, cases have been reported where human errors made by health workers have been creating havoc in the early diagnosis of many infectious diseases. A considerable population has been kept unrecognized of infection because of the lack of appropriate access to laboratories. Another problem that persists is the inaccessibility to laboratories by the rural people. This paper explores the possibilities of detecting microorganisms in blood smear by using image processing and Convolutional Neural Networks in Artificial Intelligence. A reliable and easy-to-use portable system called Mobile Microorganism Detector is developed to bring out a better diagnosis of diseases.
An Automated Security Approach of Image and Video Steganography

The steganography utilizes the craft of covered up; its principle point is to pass unnoticed information in information. There are numerous kinds of information utilized in steganography, for example, message, image, and video. Encryption is the job of securing data by shuffling it into a provision that cannot be used effectively and is called as the figure content. This mysterious figure or message content should be used by people using an anonymous key and able to decode the note into basic form. To match mystery information or single point data to the targeted alternative to various applications we use steganography. In cryptography, normally the matter in the mysterious note is mixed whereas in steganography the mysterious note is installed from diffuse standard. The proposed framework encodes a book or image within a cover image. Videos and images are exceptionally regular decisions to hide information. It is significant for the powerful and effective installation procedure to choose the video outlines which contains proper picture view that can be used to store mystery information. The audiovisual-built steganography is used due to memory prerequisites along with huge scope. LSB inclusion system, for concealing data we change LSB of video document with the data bits. This paper will concentrate on concealing data in an explicit video envelope and in an explicit advantage situation by replacing LSB. A verified copy is anticipated and created through joining less important bit and advanced encryption standard. Here we use AES for encryption plus LSB method for steganography.
Electronic Evidence in the Blockchain Era: New Rules on Authenticity and Integrity

Abstract Issues regarding the authenticity and integrity of electronic evidence have plagued judges worldwide. From a statistical viewpoint, the main cause of the judicial puzzle in dealing with electronic evidence lies in its special physical properties and judges' lack of necessary expertise. With its technical development and widespread application, blockchain technology is showing significant power and is gradually supplanting judges and expert witnesses, who have played a central role in the identification of electronic evidence's authenticity and integrity. In two recent cases, Chinese courts have confirmed for the first time that electronic data stored on or generated by blockchain met the requirements of authenticity and integrity and proposed a specific procedural test to identify blockchain evidence. Although blockchain technology may revolutionise the rules of evidence, it contains inherent risks. Its application in electronic evidence may also indicate the fusion between decentralised technology and the traditional, centralised juridical mechanisms.
Visualizing Spectral Bundle Adjustment Uncertainty

Bundle adjustment is the gold standard for refining solutions to geometric computer vision problems. This paper develops an uncertainty visualization technique for bundle adjustment solutions to Structure from Motion problems. Propagating uncertainty through an optimization- from measurement uncertainties to uncertainties in the resulting parameter estimates- is well understood. However, the calculations involved fail numerically for real problems. Often we cope by considering only individual variances, but this ignores the important mutual dependencies between parameters. The dominant modes of uncertainty in most models are large motions involving nearly all parameters at once. These frequently look like flexions, stretchings, and bendings in the overall scene structure. In this paper we present a numerically tractable method for computing dominant eigenvectors of the covariance of a Bundle Adjustment solution. We pay careful attention to the mismatched scales of rotational and translational parameters. Finally, we animate this spectral information. The resulting interactive visualizations (included in the supplemental) give insight into the quality and failure modes of a model. We hope that this work is a step towards broader uncertainty-aware computation for Structure from Motion.
Feasibility of Based Augmented Reality Devices Discovery Learning on Students Learning Outcomes in Morphology of Wijaya Kusuma Flower (<i>Epiphyllum anguliger</i>)

This study aims to describe the feasibility of Augmented reality devices which include validity, student learning outcomes, and students response to discovery learning based devices on a morphological material of the flowering plant Wijaya Kusuma (Epiphyllum anguliger). This research is the development of augmented reality device models. The target of this study is a learning tool that includes, lesson plans, student worksheets, handouts, and evaluation questions on the morphological material of Wijaya Kusuma flowers (Epiphyllum anguliger). Data collection method uses validation questionnaire with the checklist model, observation method, test and student response questionnaire. The results showed the feasibility of the device with a total average of 3.3 with good criteria. Student learning outcomes obtained an average value of 3.08 with the predicate Good (B), learning outcomes during the learning process take place, namely knowledge assessment obtained 85% per cent with good predicate, attitude assessment obtained percentage 95% very good predicate and only 5% predicate Good, and skills assessment obtained an average percentage of 90% with a very good predicate (SB). Student response to the appropriateness of the device gets an average total percentage of 95.6% with the criteria of "very strong"
SimpleNet: Hazardous-Object Detecting Neural Network in Low-Performance Devices

While the widespread of mobile devices has brought convenience in many aspects, it has also caused an increase in the number of traffic accidents due to pedestrians preoccupied with their smartphone devices. In order to solve such problem, this research proposes a network that allows training of data with reduced number of layers. SimpleNet algorithm simplifies the process of object classification into the states of detected or undetected. In order to optimize the configuration for a given device, two hyperparameters, Repeat Parameter and Feature Parameter, were adjusted throughout the testing. The test results show that the performance of full- size SimpleNet is comparable to that of Deep Neural Network. Even after reducing the model with optimized hyperparameters, SimpleNet exhibited an accuracy of 96.8%. We expect that this algorithm can be implemented in low-performance devices to detect hazardous objects on streets. Furthermore, we expect the detection target to be more limited for these devices in that they can only have a restricted vision of the surroundings.
Accident Type Definition for Implementation of Pedestrian Accident Recognition Algorithm

This paper introduces definition of accident (restricted to pedestrian falls) type through the analysis of risk factors on pedestrian accident cases. So far, there have been various developments in services to auto-recognize accidents to prevent fatal accidents of pedestrians. However, there are few existing services for this. The best reason for this is that the analysis of the accident behavior that we want to get from the results has not been done properly. Through analysis of actual accident cases, we present the risk factors that directly affect human injury, and the types of accidents are defined based on them. In order to reproduce and analyze the defined accident types using experts who had performed fall movements for many years, we have developed our own wearable devices with built-in acceleration sensors that can recognize pedestrian behavior. And, the collected data from these sensors is transmitted to smart-phone by using the bluetooth communication. Finally, the data by accident type collected and data by accident type in other papers are analyzed from the perspective of risk factors, explaining that the risk factor result values of existing studies are similar to normal data.
Buffer Overflow Attack –Vulnerability in Heap

We advise a paper which indicates a heap attack due to buffer over .Buffer overflow attack is the oldest and maximum pervasive attack technique. A buffer overflow takes place while a method or program attempts to put in writing more statistics to a set buffer, or duration block of reminiscence than the buffer is allotted to hold. Since buffers are layout to incorporate a defined quantity of records, the extra information can revoke statistics values in memory deal with which is adjacent to the destination buffer till and unless this system include enough bounds checking to flag or discard information while an excessive amount of is despatched to a reminiscence buffer. Buffer overflow attacks are classify into two categories: Heap and stack based attack .The time period heap outline pool of memory utilized in dynamic allocation for run time environment and the heap based totally attack define the flooded reminiscence space reserved for a software, but the problem arrives when appearing such an assault it make the situation critical
Image Dehazing using Dark and Bright Channel Priors and Multi-scale Filters

The paper proposes bright and dark channel priors dependent single image based dehazing in two color spaces. The hazy RGB is first converted into YC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> space. Transmission maps are estimated using dark channel priors (DCPs) and airlight of intensity (in YC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> ) and RGB components computed using three window sizes. Bright channel priors (BCPs) and threshold values are also computed for YC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> and RGB color spaces. The transmission maps are adjusted using DCPs and BCPs and refined using multiscale guided filter to obtain a superior dehazed image. Comparison (both visually and qualitatively) on variety of images exhibits the significance of proposed technique over existing techniques.
State Space Modeling of Earned Value Method for Iterative Enhancement Based Traditional Software Projects Tracking

The earned value method (EVM) is used as a standard tool for tracking of traditional software projects in order to report their status during execution. However, the status reported by EVM is not reliable as it has a fundamental limitation of not considering the partially completed tasks for the evaluation of the project status. This paper suggests considering the partially completed tasks for the earned value or budgeted cost of work performed (EV) calculation for project status evaluation. The state-space approach is used for modeling EVM, which provides foundation for integrating issues related to partially completed tasks effectively and explicit representation of the project status during planning and execution. The EVM is applied for tracking a project, and the tracking results obtained using Monte-Carlo simulation, with or without considering the partially completed tasks during execution, are presented to show the project’s execution behavior in comparison to planned behavior.
Specific Small Digital-Twin for Turbofan Engines

Ten years ago, we develop a numeric state vector to allow us following the performance of a small fleet of turbofan engines. As engine manufacturer with “flight by the hour” new contracts going, we needed to improve our technology; hence, building analytic digital-twin solutions. This improvement allows to monitor the engines as well as the behavior of the airlines. It becomes possible to identify use classes to help design systems better adapted to our clients. The main difficulty to upgrade this technology was to manage huge amount of data. At the time, the only observations we had were small 4 Kb snapshots broadcasted by satellite link. And even with such small amount of observations, the mathematics were limited to an evolving buffer of one year and a half of flights for a ten aircrafts small company. Today, we download full flight temporal measurements reaching more than 2 Gb per flight and we want to manage our fleet of more than 35,000 engines. Moreover, the life duration of the engine parts increases and the first shop visit is now more than three years away. The solution was to use new cluster technology and adapt algorithms. A main element is to be able to reduce the observation dimension hence allowing mathematic computation, but specifically for each class of behavior. The compression algorithm is now running an auto-encoder neural network on GPU and the cluster analysis still uses a self-organizing map but deployed in parallel on a cluster of computers. We propose to present this new improvement and conclude with our view about operational digital-twins.
Optimizing Resource Allocation for Data-Parallel Jobs Via GCN-Based Prediction

Under-allocating or over-allocating computation resources (e.g., CPU cores) can prolong the completion time of data-parallel jobs in a distributed system. We present a predictor, ReLocag, to find the near-optimal number of CPU cores to minimize job completion time (JCT). ReLocag includes a graph convolutional network (GCN) and a fully-connected network (FCNN). The GCN learns the dependency between operations from the workflow of a job, and then the FCNN takes the workflow dependency together with other features (e.g., the input size, the number of CPU cores, the amount of memory, and the number of computation tasks) as input for JCT prediction. The prediction result can guide the user to determine the near-optimal number of CPU cores. Besides, we propose two effective strategies to overcome the time-consuming issue of training sample collection in big data applications. First, we develop an adaptive sampling method to collect essential samples judiciously. Second, we further design a cross-application transfer learning model to exploit the training samples collected from other applications. We conduct extensive experiments in a Spark cluster for 7 types of exemplary Spark applications. Results show that ReLocag improves the JCT prediction accuracy by 4-14 percent. Moreover, the CPU core consumption decreases by 58.2 percent.
Design and implementation of video steganography using Modified CNN algorithm

The expanded popularity of computerized media has raised genuine worries over its security related issues. Security attacks through listening in, disguising and altering and in numerous different structures is normal these days. The field of computerized steganography fixates on hiding data in advanced record designs. While the use of steganographic strategies comparable to image and sound documents has been broadly inquired about, examination into the utilization of other holder less stays constrained. The point of this undertaking is to investigate various techniques for safely encoding messages in a mixed media holder, using both the sound and video stream, and utilizing stegoanalysis to decide their adequacy. In this paper, structure a modified CNN-based stegoanalyzer for images got by applying steganography with a one of a kind inserting key. The proposed design implants less convolutions, with a lot bigger channels in the last convolutional layer, and is increasingly broad, it can manage bigger image and lower payloads.
sDTM: A Supervised Bayesian Deep Topic Model for Text Analytics

Topic modeling methods such as Latent Dirichlet Allocation (LDA) are powerful tools for analyzing massive amounts of textual data. They have been extensively used in information systems and management research to identify latent topics for data exploration and as a feature-engineering mechanism to derive new variables for additional analyses. However, existing topic modeling approaches are mostly unsupervised and only leverage textual data, while ignoring additional useful information often associated with text, such as star ratings in customer reviews or categories of comments in online discussion forums. As a result, the topics extracted and new variables derived based on the learned topic vectors may not be accurate, which could lead to biased or incorrect estimation for subsequent econometric analysis and inferior performance for predictive tasks. In response, we propose a novel supervised topic modeling approach called sDTM that is designed in a Bayesian deep learning manner while incorporating additional useful data. sDTM offers three key advantages over traditional topic modeling approaches. First, it learns high-quality topics as measured quantitatively and qualitatively, which can help alleviate concerns over potential measurement errors in econometric analysis. Second, this supervised learning model achieves significantly superior predictive performance over cutting-edge baselines. Finally, sDTM is able to highlight those words that have stronger impact on the outcome, thereby facilitating transparent model investigation. Experimental results on three datasets show that sDTM not only improves supervised learning tasks, including classification and regression, but also exhibits a better model fit (e.g., lower perplexity) for document understanding. sDTM makes methodological contributions to the IS and management literature and has direct relevance for research using big data analytics.
Improved Residual Neural Network for Breast Cancer Classification

Breast cancer is one of the most dangerous types of cancer, especially for women. In 2015, it became the deadliest cancer after lung cancer in America. Some studies found that both self-detection and prevention are important factors in dealing with this cancer. The process of diagnosing breast cancer traditionally takes a long time. Moreover, pathologists are not 100% sure of the results of their diagnosis. Therefore, in this research, a computer-aided system is developed to help doctors to classify cell types based on histopathological images. In this research, a new model based on convolutional neural networks with an improved Residual Neural Network (ResNet) architecture is proposed to distinguish histopathological images into some classes of breast cancers. Testing on the BreakHis dataset shows that the best performance of the proposed method gives the average accuracies of 99.3% and 94.6% for binary and eight-class classifications, respectively. These results are comparable to state-of-the-art results in the recent study.
Virtual Reality

April 15 2021 Virtual Reality Virtual Reality. By SamuelGreengard. (Cambridge, MA, U.S.A.: The MIT Press Essential Knowledge Series, 2019. 264 pp., illus. Trade. ISBN: 978-0262537520.) Mike Mosher Mike Mosher Search for other works by this author on: This Site Google Scholar Author and Article Information Mike Mosher Online ISSN: 1530-9282 Print ISSN: 0024-094X ©2021 ISAST2021ISAST Leonardo (2021) 54 (2): 259–260. https://doi.org/10.1162/leon_r_02026 Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn MailTo Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Mike Mosher; Virtual Reality. Leonardo 2021; 54 (2): 259–260. doi: https://doi.org/10.1162/leon_r_02026 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsLeonardo Search Advanced Search This content is only available as a PDF. ©2021 ISAST2021ISAST Article PDF first page preview Close Modal You do not currently have access to this content.
Mobile Application Design for Student Learning

The research objectives were to identify, analyze needs, as well as designing a mobile application for student online learning at PT. Ruang Raya Indonesia, particularly in the process of working on tasks assigned by the teacher.The methods used in this research are data collection method using Slovin theory, systems analysis by focusing on the current business processes, designing object-oriented systems (OOAD) method.The result is the design of mobile applications in the form of an application for students to be able to do the tasks assigned by the teacher, change private tutoring schedule, and view academic progress reports of the student during private tutoring.The Conclusion of this research is PT.Ruang Raya Indonesia requires a system that can provide students the information about student's private tutoring activity.
Prune Strategy to YOLO-Based Model

One-stage networks have been widely used in target detection systems that need to learn from the big data. Most of them have good real-time performance and accuracy. However, due to their convolution structure, in actual applications, they still require more memory space and stronger computing power, thereby we choose to apply a pruning strategy on the target detection network to reduce the size of the network model and the number of parameters. In order to prove the practicability of the pruning method, we select the YOLOv3 model for experiments and provide a crop data set collected in the orchard to show the model effect. In this data set, in the best case, the volume of the network model is reduced by 52.7% compared with the original model, and the reasoning time is reduced by 57.5%. At the same time, it also uses data processing methods to compensate for the decrease in accuracy caused by pruning.
Rapid classification based on image using residual neural networks

Abstract Every human being is created with a certain gender, female or male. Gender differences provide opportunities for mating and producing offspring for regeneration. With the development of the world of information technology, the recognition of gender based on digital photos can be done for the needs of biodata, permission to access public toilets and others. The process of sex classification based on photographs with the use of residual neural networks has been carried out in this study. The process consists of several stages, namely the learning process of the features of each image that has been classified in a male or female class. The next process was carried out by resnet classification of 3,354 pictures (jpg) of men (1414 files) and women (1940 files). The data divided into 2 parts, 80% for training, 20% for testing data. The results of total images of 588 from total available data obtained an accuracy rate of 89.49%.