Reviewerly/exHarmony huggingface dataset:
Generating Interpretable Patterns for Biomedical Image Classification

In biomedical sciences, precise classification of data from normal and abnormal individuals is crucial. In this study, we address analysis of biomedical image data exploiting LAD which is a mathematical optimization-based supervised learning methodology. We propose an interpretable pattern recognition algorithm through set covering problem for practically applying large-scale biomedical data. To demonstrate the explainability and testing performance of our approach, we present computational results from analyzing breast cancer image data extracted from [3].
Automating the Selection of Container Orchestrators for Service Deployment

With the ubiquitous usage of cloud computing, the services are deployed as a virtual machine (VM) in cloud servers. However, VM based deployment often takes more amount of resources. In order to minimize the resource consumption of service deployment, container based lightweight virtualization is used. The management of the containers for deployment is a challenging problem as the container managers need to consume less amount of resources while also catering to the needs of the clients. In order to choose the right container manager, we have proposed an architecture based on the application and user needs. In the proposed architecture, we have a machine learning based decision engine to solve the problem. We have considered docker containers for experimentation. The experimental results show that the proposed system can select the proper container manager among docker compose based manager and Kubernetes.
Query-focused multi-document text summarization using fuzzy inference

The present paper proposes a fuzzy inference system for query-focused multi-document text summarization (MTS). The overall scheme is based on Mamdani Inferencing scheme which helps in designing Fuzzy Rule base for inferencing about the decision variable from a set of antecedent variables. The antecedent variables chosen for the task are from linguistic and positional heuristics, and similarity of the documents with the user-defined query. The decision variable is the rank of the sentences as decided by the rules. The final summary is generated by solving an Integer Linear Programming problem. For abstraction coreference resolution is applied on the input sentences in the pre-processing step. Although designed on the basis of a small set of antecedent variables the results are very promising.
Readout Time Measurement in Optical Camera Communication using Commercial LED Light Source

The rolling shutter mechanism makes the CMOS image sensor (CIS) suitable for optical camera communication by linking the periodicity of the image to the blinking frequency of the light source, where the readout time of CIS plays a pivot role in period estimation and information demodulation algorithms. However, in practice, the readout time is an intrinsic but unknown parameter and varied for different CISs, which should be measured and calibrated before data demodulation for different receiver devices. In this paper, a complete measurement system based on commercial LED lights is proposed to estimate the readout time of the CIS in different smart phones. Experiment results justify the feasibility of commercial LED lights in measurement and the differences in readout time for different smart phones.
Feature selection algorithms to reduce processing time in classification with SVMs

By applying feature selection algorithms, such as the Relief and the Sparse Multinomial Logistic Regression with Bayesian regularization (SBMLR) to a feature set, a smaller subset of features can be obtained. Considering only those selected for all or most of the test subjects; this shows that the Mean Absolute Value (MAV) of the signal provides less information than the rest of the features that were selected. The proposed method was applied to the classification of myoelectric signals of the transtibial section, using Support Vector Machines (SVM) as a classifier.
Mitigating theft-of-service attack - Ensuring cloud security on virtual machines

A new trend, cloud computing, has moved computer model type away from hardware and for usefulness. However only a few of the key concerns are data security, privacy, faith and a secure cloud infrastructures that could jeopardise cloud computing's widespread acceptance and benefits. Virtualization is used in cloud computing to provide customers with scalable, dependable, as well as low-cost IT providers the hypervisor is at the heart of virtualization, It seems to be many to translate abstract hardware into actual hardware and make a device. The user will get access to the VM on a longer time while you pay less by reconfiguring (i.e. store the user's processor and memory use of VM in order to credit it at the end), contributing to the theft of operation. Because during its server stay there will be no trace of the user behaviour, this assault, which is triggered by stealing a VM, would have a financial effect on the cloud system, pose a greater danger to cloud storage performance.
Image Inpainting via Enhanced Generative Adversarial Network

In this paper, we propose an Enhanced Generative Model for Image Inpainting (EGMII). Unlike most state-of-the-art algorithms using extra constraints to enforce the generator network recover semantic texture details, we construct an end-to-end network to generate both the image contents and high-frequency details progressively. Our model contains a two-phase generator and two discriminators. In our generator, the previous phase restores the structure information via convolutional encoder-decoder architecture, and the following phase captures high-frequency details via residual learning. For each generator phase, we define different objective functions and further optimize the entire network via a feed-forward manner. Moreover, for the generator in the second phase, we adopt a deep residual architecture, which also can eliminate the perceptual discontinuity on the border of the missing region. Experimental results on several public datasets demonstrate qualitatively and quantitatively that our model performs better than the state-of-the-art algorithms and can generate both realistic image contents and high-frequency details. Our code will be released soon.
Streamlining Loan Distribution Process in Agriculture Sectors

Agriculture is the keystone of human civilization and consistently improving the livelihood of rural population. As the production cost of agriculture products does not match with the return on investment, many schemes are populated by the governments of different countries to help the farmers. Though many strategies are followed to improve the financial status of farmers by promoting the production of agriculture products to face the societal demand, few undesired malfunctioning spoils the success of the entire scheme. To overcome this kind of issue, blockchain technology can be involved in all the phases of subsidies. Blockchain helps in maintaining all the loan distribution details as immutable. This helps in streamlining the agriculture credit distribution process.
Correlation Analysis between Temperature and Electricity Consumption Behavior Based on Random Matrix Theory

Electric power companies have accumulated a large number of statistical data on load characteristics, and the quality and quantity of data have been continuously improved. In this situation, how to effectively extract the correlation characteristics between data and conduct effective correlation analysis of big data is of great significance. This paper proposes a correlation analysis method based on the random matrix theory to study data statistical correlation between temperature and electricity consumption behavior. Firstly, the augmented matrix is served as the data source using load data and temperature data. Then, based on the real-time split window analysis method and the random matrix theory, the statistical characteristics of the split window matrix elements are analyzed. Finally, a quarter time span is applied to compare the influence of temperature on the behavior of electricity consumption. The linear eigenvalue statistics, taking the mean spectral radius as an example, and the pearson correlation coefficient are employed as correlation quantification indicators. According to the actual measurement load data of a certain city power grid, the adaptability and effectiveness of the method are verified, and the approach has a good robust performance.
PMatch: Semantic-based Patch Detection for Binary Programs

Binary function matching has been proposed to detect the known vulnerabilities. However, the high similarity between the vulnerable and patched versions leads to a large of false positives. Patch detection is proposed to improve the accuracy of function matching by identifying the patched functions from matching results. However, the accuracy of existing methods decreases significantly due to the function changes introduced by high compiler optimization levels.In this paper, we propose PMatch, a method based on code semantic similarity to detect the patched binary functions. Firstly, PMatch extracts patch-affected code snippets from the patched binary function. Secondly, PMatch leverages a novel unsupervised sentence embedding technique in Natural Language Processing (NLP) to generate the semantic representations of binary code. Finally, PMatch matches the patch-affected code snippets with target blocks obtained by function diffing. To evaluate PMatch, we collect 101 CVEs and compile 304 binary programs with 4 different optimization levels. PMatch achieves an 86.43% average accuracy in detecting the patched functions, which outperforms the state-of-the-art work, and costs only 65.14ms per function. Besides, at the O3 high optimization level, PMatch achieves an accuracy improvement of over 20%.
Estimates for the Branching Factors of Atari Games

The branching factor of a game is the average number of new states reachable from a given state. It is a widely used metric in AI research on board games, but less often computed or discussed for videogames. This paper provides estimates for the branching factors of 103 Atari 2600 games, as implemented in the Arcade Learning Environment (ALE). Depending on the game, ALE exposes between 3 and 18 available actions per frame of gameplay, which is an upper bound on branching factor. This paper shows, based on an enumeration of the first 1 million distinct states reachable in each game, that the average branching factor is usually much lower, in many games barely above 1. In addition to reporting the branching factors, this paper aims to clarify what constitutes a distinct state in ALE.
An Ultimately Simple Concept Drift Detector for Data Streams

This research presents a novel and simple approach to the concept drift detection problem, a fundamental issue in data stream mining because the generated models need to be updated when significant changes in the underlying data distribution occur. A number of drift detectors has been proposed but most of them have limitations such as limited effect on the accuracy results, high computational complexity, poor sensitivity to gradual change, or the opposite problem of high false positive rate. The proposed method, Ultimately Simple Drift Detector (USDD), has low false positive and negative rates as well as low computational complexity. Extensive experiments against seven drift detectors on a wide variety of datasets reveals that, using limited resources, USDD delivers high accuracy in classification and, at the same time, maintains a competitive true detection rate when compared to the other methods.
Smart Photo Editor for Differently-abled People using Assistive Technology

Differently-abled people are significant minority groups, have many limitations to live everyday life, are starved of services, and are mostly ignored by society due to many types of disabilities. This problem may lead to massive frustrations and disappointments among themselves. Assistive technology may easily fill this gap with the enhanced different aiding applications. It helped in various ways, such as helping them to interact, communicate with people and allow people to do basic daily tasks by themselves. Nevertheless, there were fewer applications developed to entertain their leisure time. Therefore, the proposed solution provides a Smart Photo Editor, a hand-free mobile application with attractive photo editing features. This application focused on people having hand motor disabilities. Users can interact with the application using simple head gestures, eye gaze, and eye blinks. Head gestures and eye blinks are used as commands for application operations, while eye gaze is used for cursor enabling, which is helpful for navigation on application screens. Machine Learning (ML) and image processing techniques are used for real-time gesture recognition and eye gaze detection, using the input video frames captured by the inbuilt front-facing camera of the mobile phone. Moreover, in addition to the image processing techniques, Mask Recurrent-Convolutional Neural Network (R-CNN) is used to develop enhanced image adjusting features and customization of backgrounds based on object detection.
Far-Sighted BiSeNet V2 for Real-time Semantic Segmentation

Real-time semantic segmentation is one of the most investigated areas in the field of computer vision. In this paper, we focus on improving the performance of BiSeNet V2 by modifying its architecture. BiSeNet V2 is a two-branch segmentation model designed to extract semantic information from high-level feature maps and detailed information from low-level feature maps. The proposed enhancement remains lightweight and real-time with two main modifications: enlarging the contextual information and breaking the constraint caused by the fixed size of convolutional kernels. Specifically, additional modules known as dilated strip pooling (DSP) and dilated mixed pooling (DMP) are appended to the original BiSeNet V2 model to form the far-sighted BiSeNet V2. The proposed dilated strip pooling block and dilated mixed pooling module are adapted from modules proposed in SPNet, with extra branches composed of dilated convolutions to provide larger receptive fields. The proposed far-sighted BiSeNet V2 improves the accuracy to 76.0% from 73.4% with an FPS of 94 on Nvidia 1080Ti. Moreover, the proposed dilated mixed pooling block achieves the same performance as that of the model with two mixed pooling modules using only 2/3 of the number of parameters.
Single Image Dehazing via Transmission Map Estimation using Deep Neural Networks

Myriads of existing approaches which perform single image dehazing make use of the widely adopted Atmospheric Scattering Model. De-hazing though this model involves the estimation of 2 key components - the transmission of haze through the scene and global atmospheric light. Depending on various factors, such as time of day, photography equipment, etc., the aforementioned components of any particular image can vary wildly and can prove quite difficult to correctly estimate. Some previous approaches that exist make use of either premeditated priors to estimate these components or end-to-end neural networks to fully reconstruct a de-hazed image. This paper delves into the use of a deep convolutional, U-Net based segmentation network to obtain the medium transmission map from the input hazy image is explored. The model is trained using a supervised approach with a samples of hazy scenes and their corresponding medium transmissions. The estimation of global atmospheric light of a scene is done using a modification of the dark channel prior method, making use of the Y(luma) component of the YUV representation of the hazy image. Experimental and benchmarking results on 3 different testing datasets are presented, which show that this system can produce high quality haze-free images and can do so efficiently and reliably.
Secure and Fraud Proof Online Payment System for Credit Cards

Credit card fraud is one of the most critical threats affecting individuals and companies worldwide, particularly with the growing number of financial transactions involving credit cards every day. The most common threats are likely to come from database breaches and identity theft. All these threats put the security of financial transactions at severe risk and require a fundamental solution. This paper aims to suggest a secure online payment system that significantly improves credit card security. Our system can be particularly resilient to potential cyber-attacks, unauthorized users, man-in-the-middle, and guessing attacks for credit card number generation or illegal financial activities by utilizing a secure communication channel between the cardholder and server. Our system uses a shared secret and a verification token that allow both sides to communicate through an encrypted channel. Furthermore, our system is designed to generate a one-time credit card number at the user’s machine that is verified by the server without sharing the credit card number over the network. Our approach combines machine learning (ML) algorithms with unique temporary credit card numbers in one integrated system, which is the first approach in the online credit card protection system. The new security system generates a one-time-use credit card number for each transaction with a predetermined amount of money. Simultaneously, the system can detect potential fraud utilizing ML algorithm with new critical features such as the IMEI or IP address, the transaction’s location, and other features.
Mobile Application Development for VR in Education

Virtual Reality (VR) is gaining popularity due to the rapid improvement of computing capabilities. VR refers to a three-dimensional environment created by a computer-generated simulation. The user interacts with the artificially created environment using sensors. The user can explore the virtual world, move in it, and even interact with certain virtual features. This paper describes the early development of a mobile application that uses VR for education. It also reviews VR's evolution, the possibilities of combining 5G with VR, the application of VR in education, and its potential uses.
Pelatihan Penggunaan ‘Kahoot’ dalam Mengajar Reading Narrative bagi Siswa Kelas X SMA Veteran 1 Sukoharjo

During this pandemic, all sectors have collapsed, even the education sector, which is accustomed to face-to-face classes, has become mandatory for offline or online classes, so English teachers are also required to be more creative in developing learning media. Although English is a compulsory subject, the results attained from English subject are not very satisfying. Learning activities is one of the stages that determines the success of student learning. The quality of education and teaching can be enhanced by optimizing several elements such as students, teachers, learning indicators, lesson content, methods, media, and evaluation. Because of the importance of reading material for students, the Bangun Nusantara Sukoharjo Veteran University service team offers Kahoot application training for class X SMA Veteran 01 Sukoharjo. The method utilizsd in this method is a quiz that is taught to class X students according to the lesson plans and syllabus of class X students, as well as the reading material given. The target to be achieved from this service is to increase knowledge and instruction about reading training using Kahoot media for class X SMA Veteran 01 Sukoharjo. In addition, this dedication is expected to be able to achieve several things, nemely: (1) the teacher knows more about the ability of each student in reading comprehension; (2) teachers can think logically and systematically by utilizing existing media in answering quizzes through reading training; (3) students are assisted in learning to think from a point of view in thinking practice; (4) teachers can further develop students' motivation to learn; and (5) students are actively involved in class planning and management.
VR Welding Kit: Accuracy Comparison Between Smartphone VR and Standalone VR Using RMSE

Currently, smartphone Virtual Reality (VR) has simple input methods and not able to track hand positions effectively. This limits the user's interaction in VR. This paper presents the use of multiple marker tracking methods to solve the problem while using welding as the use case study. VR Welding Kit is a VR application that uses a smartphone's camera and two external inputs: a tracked handheld controller integrated with multiple markers technique called welding torch to track user's hand position in real-time and a paper marker called welding position. VR Welding Kit simulates real-life welding training for trainees to gain basic psychomotor skills. The VR Welding Kit application is installed into two different mobile VR devices, smartphone VR and a standalone VR, for tracked handheld input accuracy comparison. 24 participants were involved in this study. Ideal welding variables of travel speed, work angle, and travel angle were used as the benchmark data. The evaluation was conducted by using the Root Mean Square Error (RMSE) method. The study shows that smartphone VR using multiple markers tracking method has higher accuracy than standalone VR.
Bayesian Personalized-Wardrobe Model (BP-WM) for Long-Term Person Re-Identification

Long-term surveillance applications often involve having to re-identify individuals over several days. The task is made even more challenging due to changes in appearance features such as clothing over a longitudinal time-span of days or longer. In this paper, we propose a novel approach called Bayesian Personalized-Wardrobe Model (BPWM) for long-term person re-identification (re-ID) by employing a Bayesian Personalized Ranking (BPR) for clothing features extracted from video sequences. In contrast to previous long-term person re-ID works, we exploit the fact that people typically choose their attire based on their personal preferences and that knowing a person’s chosen wardrobe can be used as a soft-biometric to distinguish identities in the long-term. We evaluate the performance of our proposed BP-WM on the extended Indoor Long-term Re-identification Wardrobe (ILRW) dataset. Experimental results show that our method achieves state-of-the-art performance and that BP-WM can be used as a reliable soft-biometric for person re-identification.
A Classified Question and Answer System Based on SpringBoot Integrated Neo4j Graph Database

With the development of artificial intelligence, natural language processing is more and more widely used in daily life. Firstly, this paper explains the project background of the classification question answering system based on SpringBoot integrated with Neo4j graph database, and introduces the relevant parts of the technology. And then focus on intelligent question answering system to build the actual system on film industry. User input would firstly use HanLP word recognition to split out the statement, then match the template using the naive bayesian classifier to identify most appropriate template, finally to replace the phrase with a phrase or secondary database query. This automatic question and answer system provides user a more convenient way to know about the movie information user interested in. In this paper, HanLP and Naive Bayes are used to implement an intelligent classified question-answering system. Other functions in the system include automatic daily update of movie information, classic movie dialogue and related movie videos are integrated by using third-party API. The actual operation of the system is smooth and the user experience is good.