Reviewerly/exHarmony huggingface dataset:
Automated Detection of Helminth Eggs in Stool Samples Using Convolutional Neural Networks

Schistosomiasis, trichuriasis, and ascariasis are few of the many neglected tropical diseases that still affect populations in poor countries. These diseases cause a variety of symptoms such as abdominal pain, may lead to complications, and may even result in death in severe schistosomiasis cases. To complement the efforts of governments and health organizations in mitigating the morbidity and transmission of neglected tropical diseases, several applications utilizing machine learning techniques have been developed in recent years to automate the detection of parasites in microscopy samples. In this paper, we explore the use of YOLO, a convolutional neural network framework, in the detection of helminth eggs in stool samples. We collected and labelled a dataset with varying imaging conditions due to different staining conditions and acquisition by smartphone cameras with different parameters. We demonstrate that the approach works well despite this variance in imaging conditions in the dataset, achieving high sensitivity in the detection of helminth eggs and high accuracy in the identification of egg species. The trained model operates in real-time, making it suitable for automated diagnosis and real-time annotation.
RETRACTED: Face detection based on open Cl design and image processing technology

Face detection is a system that automatically recognizes facial expressions. Extract our structure and describe its function of the contours of the eyebrows, eyes and mouth with elastic rectangles. This manual facial features are an improvement over the hybrid mask model method. For some have to reduce the recognition time and define the accuracy of the relevant cognition. Then, these vectors determine the network, which is being used to implement user facial expression and neural interactions. In short, FEID (Facial Expression Image Database) information is used in this study to determine face and face identification. 96.2% of test results and 92.8% of the FEID database show that personal facial recognition tests and official member face recognition can be approved. Face recognition 97.4% of FEID samples were widely recognized. Real-time face detection is a new method using a variety of computations. This algorithm uses the Local Binary Format (LBF) as a face detection feature vector. Code used using OpenCL units. Illumination is the use of irreversible gamma correction or Gaussian variations of different illumination conditions creating strong protocols. This implementation has proven to be faster compared to previous co-executions.
Simulation of Chinese online teaching invocational colleges based on complex evolution and improved neural network

Because the teaching methods of Chinese teaching in higher vocational colleges are improper and the problems in the teaching process cannot be found in time, the teaching effect is not good and there are many difficulties in the teaching process of teachers. According to actual needs, based on complex evolution and improved neural network algorithms, this paper builds a Chinese online teaching system in high vocational colleges. After communication with users and social research, the model function module is determined, and the system performance is improved through the mapping optimization model to eliminate data transmission problems in the system operation, and the realization process of the system function module is shown in the form of a diagram. In addition, this paper designs a control experiment to verify the performance of the model and uses the traditional algorithm model as a control to start the test teaching and uses statistical methods to compare the teaching effects. The research results show that the teaching system constructed in this paper has certain advantages over the traditional model.
Amorphous Region Context Modeling for Scene Recognition

Scene images are usually composed of foreground and background regional contents. Some existing methods propose to extract regional contents with dense grids or objectness region proposals. However, dense grids may split the object into several discrete parts, learning semantic ambiguity for the patches. The objectness methods may focus on particular objects but only pay attention to the foreground contents and do not exploit the background that is key to scene recognition. In contrast, we propose a novel scene recognition framework with amorphous region detection and context modeling. In the proposed framework, discriminative regions are first detected with amorphous contours that can tightly surround the targets through semantic segmentation techniques. In addition, both foreground and background regions are jointly embedded to obtain the scene representations with the graph model. Based on the graph modeling module, we explore the contextual relations between the regions in geometric and morphology aspects, and generate the discriminative representations for scene recognition. Experimental results on MIT67 and SUN397 demonstrate the effectiveness and generality of the proposed method.
Augmented Reality for Vocational Education Training in K12 Classrooms

Vocational Education Training (VET) is becoming a more integral part of the curriculum to account for the requirement of skilled workforce in the global scenario. Augmented Reality (AR) can serve as a valuable medium to integrate this aspect into mainstream educational systems. In this work, we explore the feasibility of AR for vocational skill training of students at the K12 level based on the state of the art literature analysis. Furthermore, we propose opportunities concerned with the aspects of effective integration and usability of these training modules into the traditional pedagogical systems.
Coq to C translation with partial evaluation

Coq proof assistant can be used to prove various properties of programs written in the Gallina language. It is also possible to translate Gallina programs to OCaml programs. However, OCaml is not suitable for low-level programs. Therefore, we are developing a Coq plugin for Gallina to C translation. This plugin transforms functions written in Gallina into a form as close to C as possible within Gallina. This transformation includes partial evaluation, which improves execution efficiency and eliminates polymorphism and dependent types. We can easily verify in Coq that this transformation does not change the execution result, and thus it is highly reliable. And Gallina functions after this transformation can be easily translated to C.
Application of a Modified Generative Adversarial Network in the Superresolution Reconstruction of Ancient Murals

Considering the problems of low resolution and rough details in existing mural images, this paper proposes a superresolution reconstruction algorithm for enhancing artistic mural images, thereby optimizing mural images. The algorithm takes a generative adversarial network (GAN) as the framework. First, a convolutional neural network (CNN) is used to extract image feature information, and then, the features are mapped to the high-resolution image space of the same size as the original image. Finally, the reconstructed high-resolution image is output to complete the design of the generative network. Then, a CNN with deep and residual modules is used for image feature extraction to determine whether the output of the generative network is an authentic, high-resolution mural image. In detail, the depth of the network increases, the residual module is introduced, the batch standardization of the network convolution layer is deleted, and the subpixel convolution is used to realize upsampling. Additionally, a combination of multiple loss functions and staged construction of the network model is adopted to further optimize the mural image. A mural dataset is set up by the current team. Compared with several existing image superresolution algorithms, the peak signal-to-noise ratio (PSNR) of the proposed algorithm increases by an average of 1.2–3.3 dB and the structural similarity (SSIM) increases by 0.04 = 0.13; it is also superior to other algorithms in terms of subjective scoring. The proposed method in this study is effective in the superresolution reconstruction of mural images, which contributes to the further optimization of ancient mural images.
Performance of subcarrier-wave quantum key distribution in the presence of spontaneous Raman scattering noise generated by classical DWDM channels

In this paper, we study the performance of the subcarrier-wave quantum key distribution system (SCW QKD) in the presence of spontaneous Raman scattering (SpRS) noise generated by classical channels of the dense wavelength division multiplexing (DWDM) network within a single-mode optical fiber. We present the mathematical model for evaluation of the quantum bit error rate and secure key generation rate with the SpRS noise taken into account. We consider two regimes of the SCW QKD system: the continuous wave regime, which uses a continuous wave laser, and the pulsed regime. For these regimes, performance of the system is analyzed depending on receiver sensitivity of classical DWDM. It is found that the pulsed regime outperforms the continuous wave regime in both the secure key generation rate and maximum achievable distance.
I2S2: Image-to-Scene Sketch Translation Using Conditional Input and Adversarial Networks

Image generation from sketch is a popular and well-studied computer vision problem. However, the inverse problem image-to-sketch (I2S) synthesis still remains open and challenging, let alone image-to-scene sketch (I2S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) synthesis, especially when full-scene sketch generations are highly desired. In this paper, we propose a framework for generating full-scene sketch representations from natural scene images, aiming to generate outputs that approximate hand-drawn scene sketches. Specifically, we exploit generative adversarial models to produce full-scene sketches given arbitrary input images that are actually conditions which are incorporated to guide the distribution mapping in the context of adversarial learning. To advance the use of such conditions, we further investigate edge detection solutions and propose to utilize Holistically-nested Edge Detection (HED) maps to condition the generative model. We conduct extensive experiments to validate the proposed framework and provide detailed quantitative and qualitative evaluations to demonstrate its effectiveness. In addition, we also demonstrate the flexibility of the proposed framework by using different conditional inputs, such as the Canny edge detector.
User Authentication: A Three Level Password Authentication Mechanism

Abstract User authentication is one of the cutting edge research to provide access to legitimate uses. A number of mechanisms like biometric, image based, graphical based have been implemented. In this research work a three level password authentication is proposed and reported experimental results. From the result analysis it is found that the three level authentication provides reliable security level in comparison to the existing mechanisms.
Visualization to support decision-making in cities: advances, technology, challenges, and opportunities

Data is a valuable asset to the management of a city. With the growing integration of technology, some tools help to collect, process, and visualize urban data, aiding the interpretation and understanding of how urban systems work. Despite the wide use of visualization to support decisionmaking in urban management, the understanding of urban data visualization in cities is limited in the current literature. Therefore, this paper analyzes the relevant body of literature to identify the advances in information visualization for decisionmaking in cities, the visualization technology used, and the areas of opportunity in the field of study. As a result of this analysis, we identified eleven visualization technologies used for urban data visualization. The literature review suggests that specialized visualization tools do not consider the users and their real needs. For this reason, we recommend incorporating the User-Centered Design approach in urban data visualization design.
Swapping Face Images Based on Augmented Facial Landmarks and Its Detection

Facial landmark points that are precisely extracted from the face images improve the performance of many applications in the domains of computer vision and graphics. Face swapping is one of such applications. With the availability of sophisticated image editing tools and the use of deep learning models, it is easy to create swapped face images or face swap attacks in images or videos even for non-professionals. Face swapping transfers a face from a source to a destination image, while preserving photo realism. It has potential applications in computer games, privacy protection, etc. However, it could also be used for fraudulent purposes. In this paper, we propose an approach to create face swap attacks and detect them from the original images. The augmented 81-facial landmark points are extracted for creating the face swap attacks. The feature descriptors Weighted Local Magnitude Patterns (WLMP) and Support Vector Machines (SVM) are utilized for the swapped face images detection. The performance of the proposed approach is demonstrated by different types of SVM classifiers on a real-world dataset. Experimental results show that the proposed system effectively does face swapping and detection with an accuracy of 95%.
Historical Places &amp; Monuments Identification System

Sri Lanka, which is known as “the pearl of the Indian ocean” provides great survival and civilization history dating back to the 3rd century. Most of the archaeological sites are attracted by not only Sri Lankans but also by tourists. When searching for the information about the archaeological sites, there are lack of trusted information sources and smart online platforms. Even though some information is available, no convenient and efficient ways to retrieve them. When trusted information is provided in a user-friendly manner, the value will be added to the Sri Lankan economy. Since the world is driving towards the "E-Era", everything is involved with Information Technology. The proposed system contributes to solve the above problems with Artificial Intelligence & Machine Learning concepts. The system is assisted using four major components namely, image identification, community platform, conversational bot, and image visualization. The image Identification component identifies the archaeological sites using image processing techniques. The community platform gathers trusted information from archaeologists and deep learning techniques are used to deliver that content to the users. The artificial intelligence conversational bot is established to communicate and retrieve available information in a convenient manner. The image visualization component is used to provide reality visualization on archaeological sites using the augmented reality techniques. The techniques and the algorithms are evaluated to deliver better performance with brilliant user experience.
Maximizing Reliability of Data-Intensive Workflow Systems with Active Fault Tolerance Schemes in Cloud

Most existing researches on cloud workflow systems have focused on resource scheduling with the aims to minimize system delay under budget constraints or optimize system cost under deadline constraints. However, cloud providers cannot guarantee a failure-free cloud environment, a compact scheduling plan is prone to failure, thus, workflow system reliability has been identified as a critical and challenging issue in the volatile cloud environment. With the ability of cloud, it is easy for users to implement the active fault tolerance schemes, e.g., Scale-Out. However, it will lead to issues like security problem and extra management cost. In this paper, we first investigate Scale-Up and Scale-Hybrid schemes to fully explore the possibilities offered by the ability of cloud. We formally model the problem of optimizing the reliability of a cloud workflow system under budget constraints with these three fault-tolerance schemes. These optimization problems are discrete and non-convex. Thus, we propose a genetic algorithm based method for workflow fault tolerance (GA4WFT). Finally, we evaluate the effectiveness and efficiency of proposed GA4WFT with three different fault-tolerance schemes through experiments conducted on Amazon EC2 data.
Sentiment Analysis in Jira Software Repositories

Mining Software Repositories is a field of study whose main task is to extract valuable information from a large amount of data available within software repositories. This information about systems and projects can be exploited in different ways improving the development processes in Software Engineering. A new research area makes use of this data for analyzing the software professionals' emotional state and their relationship with different factors such as productivity and quality in tasks. In our study, we applied a supervised classification model to predict sentiments contained in issue comments in open source projects hosted in Jira repositories. The main objective is verifying if sentiments are related to issue comments and factors such as issue resolution types (Resolved or Unresolved) as well as the time of day and the day of the week, in which the comments were written. Our results show that comments in unresolved issues tend to express less positive and more negative sentiments regarding the comments in resolved issues. In addition, we also obtained interesting results regarding the sentiments in comments and the time/day of the week of publication.
Scholarly Paper Recommendation via Related Path Analysis in Knowledge Graph

Recommending helpful and interesting scholarly papers for researchers from a large number of scholarly papers is the main way to improve research efficiency. Traditional collaborative filtering or content-based recommendation methods do not have a better-fused knowledge graph and have method bottlenecks such as cold start and poor interpretation. Based on the knowledge-aware path recurrent network (KPRN), this paper proposes a method for recommending scholarly papers that combines user preferences and knowledge graph path information. Firstly, a delayed extension bi-directional breadth-first search path algorithm is proposed to find the path between two nodes in the knowledge graph with low time complexity. Then, the user preference vector is generated by the user's historical paper operation. Finally, the LSTM cyclic neural network model is used to extract the information of multiple paths and combine it with user preferences to obtain the list of recommended papers. The experimental results show the validity and good interpretability of this method.
Implementation Of Convolutional Neural Network Method For Classification Of Baum Test

One of the conditions for hiring workers in Indonesian companies is to pass a psychology test. Psychology test has many types of tests, one of which is the Baum test, a tree-drawing test. Baum test requires participants to draw a tree following the imagination of the participants. Later, the picture of the participant tree obtained from the Baum test will be assessed by psychologists as one of the labor assessment tools. This assessment is based on the shape of the tree crown, leaf shape, tree size on paper, etc. Currently, the assessment of the Baum test is still done manually by a psychologist. In order to help psychologists in assessing the Baum test, we propose an application in the form of a model that can study and classify tree images based on the tree size in the image by using the Convolutional Neural Network (CNN) method. CNN method allows computers to classify an object. The data are provided in the form of digital images of tree images from Baum testing that has been conducted. The data that has been collected will be used to train the model. The model used in this study has an architecture in the form of a convolution layer with a kernel size of 3×3, a pooling layer with a parameter size of 2×2, a dropout layer, a flatten layer, and a dense layer. The model was trained with 60 epochs and had a test and training data scale of 70:30. The model in this research has an accuracy of 74.07% in predetermined data testing and has an accuracy of 75% in new data testing.
Informative Mobile Robot Exploration for Radiation Source Localization with a Particle Filter

In this work, we consider the localization problem of an unknown radiation source with measurement uncertainty by using robotic systems in a geometric environment. We proposed the scheme for localization of a radioactive source using the particle filter with information gain-based exploration. The traditional method to localize the radiation is to use the gradient descent algorithm. However, such the algorithm may fail to work in the case of uncertain measurements, which lead to an inaccurate outcome. On the other hand, a standard particle filter can be used to deal with the measurement uncertainty, but the estimated intensity result may be unstable since it only uses the current measurement update as a likelihood function. To solve the problem of measurement uncertainty and unstable intensity result, we proposed an exploration method using the information gain with particle filter. The algorithm takes the information of the particles in the filter to estimate the possible actions for the robot. The expected information gain from those actions can be used to select the best possible action for the robot. The proposed method has been verified by the simulations. The proposed strategy can decrease the time it takes to finish the task comparing to the conventional methods such as the lawn mowing algorithm and source estimation seeking algorithm.
Knowledge Distillation based Compact Model Learning Method for Object Detection

Recently, video analysis technology through deep learning has been developing at a very rapid pace, and most of the technology related to improving recognition performance in server environment is being developed. However, in addition to video analysis technology in the existing server environment, the demand of object detection in visual image analysis have been increasing recently in embedded boards of low specification and mobile environments such as smartphones, drones, and industrial boards. Despite the significant improvement in the accuracy of existing object detectors, image processing for real- time applications often requires a lot of runtime. Therefore, many studies are being conducted on lightweight object detection technology, and knowledge distillation is one of the solutions. Efforts such as model compression use fewer parameters, but there is a problem that accuracy is significantly reduced. In this paper, we propose method to improve the performance of lightweight mobilenet-SSD models in object detection by using knowledge transfer methods. We conduct evaluation with PASCAL VOC dataset. Our results show detection accuracy improvement in object detection.