Reviewerly/exHarmony huggingface dataset:
Plasmodium Parasite Detection Using Combination of Image Processing and Deep Learning Approach

The development of an intelligent system for automated malaria detection became the one of challenges since its application supported the examination process which was conducted manually by the doctor or medical personnel. Some previous studies have been done to overcome those problems. However, most of them still have problem in detecting parasite candidates. Hence, their proposed methods did not successfully detect all parasite candidates and remains a large number of false-negative. Actually, the misdetection problem occurred since the characteristic of parasites seems unclear. To overcome these problems, we applied image processing technique and deep learning architecture to detect and to ensure whether the detected candidate is a parasite or not. Our proposed method was applied to 46 digital microscopic images provided by the Department of Parasitology, Universitas Gadjah Mada and Eijkman Institute for Molecular Biology. The proposed method comprised of four steps which are normalization process using GGB (green, green, blue) color transformation, segmentation process using Otsu followed by some morphological operations, object labelling using BLOB analysis, and classification using deep learning. Our detection process successfully detected all parasites and the classification process achieved an accuracy, sensitivity, specificity, PPV and NPV of 98.97, 100, 98.08, 97.85, and 100% respectively. This result shows that our proposed method achieved outstanding performance in both detection and classification process which indicates that our proposed method had the potential to be implemented as an intelligent system for supporting the parasitologist in conducting rapid assessment of plasmodium parasite infection.
AN ENHANCED IMAGE STEGANOGRAPHY TECHNIQUE FOR HIGH-SECURITY COMMUNICATION

Purpose of the study: We propose an approach to hide data in an image with minimum Mean Squared Error (MSE) and maximum Signal-to-Noise ratio (SNR) using Discrete Wavelet Transform (DWT).&#x0D; Methodology: The methodology used by us considers the application of Discrete Wavelet transform to transform the values of the image into a different domain for embedding the information to be hidden in the image and then using Singular Value decomposition we decomposed the matrix values of the image for better data hiding.&#x0D; Main Findings: The application of the SVD function gave the model a better performance and also RED pixel values with the High-High frequency domain are a better cover for hiding data.&#x0D; Applications of this study: This article can be used for further research on applications of mathematical and frequency transformation functions on data hiding. It can also be used to implement a highly secure image steganography model.&#x0D; Novelty/Originality of this study: The application of Discrete Wavelet Transform has been used before but the application of SVD and hiding data in the H-H domain to obtain better results is original.
Reliability Evaluation of Visualization Performance of Convolutional Neural Network Models for Automated Driving

As deep learning methods in image recognition have achieved excellent performance, researchers have begun to apply CNNs(convolutional neural networks) to automated driving. However, the explainability for the decision making of automated driving is highly desired. In order to trust the model in automated driving, visualization methods have become important for understanding the internal calculation process of CNNs. Therefore, in a previous study, we proposed a method to evaluate the visualization performance of CNN models by using a mathematical model instead of a human driver to generate a dataset that can determine the ground-truth point in images. However, the reliability of the proposed method for validating the visualization performance was not provided. Therefore, in this paper, we verify the proposed method through two experiments to demonstrate the task-dependent performance and visualization performance during training. The reliability of the visualization performance has been demonstrated through experimental results. Therefore, we proposed an evaluation method for visualization performance in automated driving systems.
Super-resolution reconstruction using channel splitting residual edge-guided network for wideband magnetic resonance imaging

In order to improve the accuracy of surgical analysis and clinical diagnosis, it is very important to obtain brain structure images. And magnetic resonance imaging (MRI) is one of the most commonly used methods. Moreover, High-Resolution (HR) MR image with smaller pixel size provides more important structure and texture details [1] and helps early diagnosis and subsequent analysis. But in fact, it is relatively difficult to obtain high-quality MR images due to many factors, such as hardware equipment, imaging time, required signal to noise ratio (Signal to Noise Ratio, SNR) and motion artifacts, etc. Generally speaking, brain MR images are often obtained with thick slice thickness and lower image quality to reduce the scanning cost and sampling time. However, this is not conducive to further medical analysis. For decades, Super- Resolution (SR) related technologies have been used to improve the quality of MR images to restore important structural information and facilitate clinical diagnosis. Also, as deep learning (DL) develops significantly nowadays, DL-based methods are widely applied to SR issues. Therefore, we propose a convolution neural network (CNN) SR method called Channel Splitting Edge-guided Residual Network (CSERN). Besides, we combine our method with a novel accelerating imaging method called Single-frequency Excitation WideBand (SE-WB) MRI and design a loss function to achieve higher performance on several index such as structural similarity index (SSIM) and Peak signal-to-noise ratio (PSNR).
Application of Hidden Markov Model to Analyze the Biometric Signature: A Comprehensive Survey

Biometrics is useful in identifying and authenticating a person by a set of unique and specific data. This technology uses input in various forms like face, iris, retina, fingerprint, speech, signature, handwriting, DNA, and other biological or physical characteristics. This paper discusses the biometric signatures analyzed by applying a hidden Markov model in different ways. A biometric signature is useful for person identification, access control mechanism, computer devices, banking services, military and intelligence services, health, education, and social services, etc. Even though identification of a person can be achieved through a rule-based mechanism guided by manual efforts, but characteristics like universality, distinctiveness, permanence, and collectability minimize error scenarios, and easy integration makes it the lucrative state-of-the-art choice. The main advantage of the biometric signature is being clear, and it ascertains discrimination between authenticated and fraudulent access to the resource. Many countries have already passed the “Electronic signature act” (Jonathan in Berk Tech LJ 16:391, 2001) to make an electronic signature as legally binding to use nationally and internationally as a traditional signature. Hence, the best possible performance and accuracy are demanded from the biometric signature authentication model.
The Results of Exit Polls in Kansas to Verify Voting Machine Counts in the November 2016 Election

Abstract Citizens’ exit polls are performed by local voters to verify the official reported election results. Five citizens’ exit polls were run in southeast Kansas during the Nov 8th 2016 election. These exit polls were designed specifically to verify computer generated vote counts and run solely by volunteer labor, all local citizens who were willing to put in the necessary hours on Election Day to conduct the poll and later, to count the results by hand. These exit polls were able to obtain high participation rates resulting in the ability to detect small yet statistically significant differences. All five polling stations surveyed show evidence of multiple statistical anomalies in both the pattern and size of the errors between the official results and exit poll results although biases were not uniformly oriented across sites. The small discrepancies found in the studied races were insufficient to alter the outcomes. Non-response bias and unintentional errors were evaluated as potential causes; those explanations were plausible in some but not all cases. These results show a pattern of discrepancies between the exit polls and computer counted results displaying consistent bias within sites. This would be an expected outcome of a deliberate manipulation of the computer results. While this data doesn’t conclusively prove election interference and manipulation of votes counts, it should be taken seriously as a sign of such interference. Doubts about the accuracy of the reported results are appropriate unless other plausible explanations for the discrepancies can be found.
Application of Nonimmersive Virtual Reality Technology in an Environmental Design Course: A Study of Gender Difference

With the Coronavirus outbreak, colleges and universities have switched offline teaching to online courses. Nonimmersive virtual reality technology possess characteristics such as convenience and interactivity. Therefore, the present researchers investigated the technology as a teaching tool with students enrolled in an environmental design course at Shunde Polytechnic. An empirical study was conducted to determine whether the learning of male and female students differed when performing a project site analysis using the technology. The aspects of learning evaluated were accuracy and speed in performing the site analysis as well as usage experience. The results indicated that the students did not differ significantly in accuracy or usage experience; however, male students performed the analysis more quickly than did female students. Nonimmersive virtual reality technology can serve as an effective aid in teaching environmental design. However, more time should be allocated to female students conducting site environment analyses.
LEARNERS’ PERCEPTION AND PRACTICES ON AUTONOMOUS LANGUAGE LEARNING IN EFL SETTINGS

This research was aimed to investigate learners’ perception and practices of understanding teachers’ teaching objectives and requirements related to autonomous language learning, setting up their objectives and study plans to autonomous language learning, and find out learners perceive in learning English autonomously. To gain a deep understanding and utilizing the objectives, the researcher employed descriptive qualitative research. The research findings were extracted through both an online questionnaire and interview. Some constraints were reported in this research, but more positive responses were still shown by the learners. They positively disposed to the notion of learner autonomy. The learners specifically showed positive attitudes toward instructors’ teaching objectives and requirements, setting up their objectives and study plans, and their practices in learning English autonomously. Further researchers, thus, need to research in various universities with a large number to gain a deeper and wider understanding about learners perceive in autonomous language learning.
KNN-ROBKP: An Approach for Improvement of Spam Detection over Documents

Data spamming over the document is one of the common issue among various articles. Many item such as mail, authentic communication and other media of writing needed proper and unique content without any spamming information. Documents contain information such as privacy preserving data, resource oriented information having the spamming issue as well as duplicity issue. In such scenario the spamming analysis algorithm plays an important role which make easy to identity and stop the data to process. NLP which is natural language processing and spam stoppage help analysis of words, content and its meaning to decide the authenticity. Many approaches used exactness algorithm approach which is limited to some extent. Thus the new KNN based model with NLP is used in given research. Solution for Robin Karp. A hybrid approach is then provided with the term KNN-ROBKPP. The implementation is carried out using the Java language as a parameter with calculation time and similarity measurement. The results obtained indicate that the efficacy of the suggested method is greater than that of the standard N-Gram solution.
An Approximate Implementation of Multi-sensor Generalized Labeled Multi-Bernoulli Filter for Multiple Target Tracking

Multi-sensor generalized labeled multi-Bernoulli (MS-GLMB) filter provides an analytic solution for multi-sensor multiple target tracking problems, which outputs the number of targets and the trajectories of their states by using multiple sensor observations. However, the complicated data association makes MS-GLMB filter intractable. In this paper, we propose an approximate implementation MS-GLMB filter to effectively track multiple targets in multi-sensor systems. The proposed MS-GLMB filter is compared with the sequential version of the MS-GLMB filter, and simulation results show that our method has improved tracking performance.
Computer Multimedia Courseware in Genetic Algorithm Mathematical Model of Pattern Theorem

Personalized courseware is an important research field based on computer multimedia, because fixed courseware is not suitable for all students. The purpose of this paper is to study the mathematical model of genetic algorithm in the pattern theorem of computer multimedia courseware. Using the genetic algorithm of pattern theory, the mathematical model corresponding to the problem of multimedia courseware is designed and constructed. The traditional genetic algorithm is improved and verified by experiments. The improved algorithm is applied to the final paper output module system of multimedia computer. Taking the students of the course data structure in the Institute of information automation as the experimental object, a personalized multimedia computer program based on genetic algorithm is developed by designing the motion function. The experimental results show that if we continue to complete the remaining iterations on the premise of reaching the fitness value, the fitness value of the test paper generated by snga will be closer to the optimal value of the fitness function. After selecting the personalized multimedia courseware on the computer, the number of people who got higher scores in the post test than in the same pre-test was 64.
PoratRank to Improve Performance Recommendation System

The e-commerce recommendation system has experienced tremendous progress and has caused an explosion of information, making it difficult for users to choose items that fit their preferences and take a long time. One way to overcome this condition is to use a collaborative filtering approach. Collaborative filtering generally uses similarity measurements and ranking predictions to produce recommendations. However, the recommendations presented are less reliable when data conditions are sparse. This condition encourages the development of ranking-based collaborative filtering. Some ranking-based methods are Copeland and Borda, which carry out an aggregation process to produce product ratings that are recommended to users. Both of these methods use limited ranking data at the user preference profile stage and do not involve re-ranking data during the aggregation process. This process causes the resulting recommendations to decrease in quality. Therefore, this paper proposes the PoratRank method. The basic idea of this method is to optimize the utilization of ranking data to produce product ratings that are more in line with user preferences. Ranking data is used as an additional factor in determining product points. Determination of product points not only looks at the ranking value but also considers the same number of ratings, and the position of the product in its appearance. It also sees the effect of the ranking value using the minus function. Optimizing ranking data in the aggregation process can improve the recommendation results, as shown by the average value (NDCG) of the PoratRank method, which is higher than the Borda and Copeland methods. The PoratRank method is faster than the Copeland method and manages to overcome the problem of sparsity and scalability, which is a major problem in the collaborative filtering approach, so the PoratRank method is feasible to be used in improving performance recommendations system.
Design and Testing of a Mobile BPM and Peripheral Oxygen Saturation Monitoring for Patients in Bangladesh

This paper presents the findings of a successfuldesign and testing for a prototype of a wearable band that can be used to monitor heart beats per minute (BPM), peripheral oxygen saturation (SpO2), and temperature for patients in Bangladesh. In the mobile app version, this sensor data can be converted from text to speech with the press of a button. The device can also send alerts to a person of choice with the patient's current geographic position and locates the nearest hospital from that position. The prototype was compared with a hospital certified device and the results were quite promising. The uniqueness of this design is also highlighted in this paper by comparing the features and results with similar research work.
Broadcasting of NPT entanglement in two qutrit systems

It is known that beyond $$2 \otimes 2$$ and $$2 \otimes 3$$ dimensional quantum systems, Peres–Hordecki criterion is no longer sufficient as an entanglement detection criterion as there are entangled states with both positive and negative partial transpose (PPT and NPT). Further, it is also true that all PPT entangled states are bound entangled states. However, in the class of NPT states, there exist bound entangled states as well as free entangled states. All free/useful/distillable entanglements are part of the class of NPT entangled states. In this article, we ask the question that given an NPT entangled state in $$3 \otimes 3$$ dimensional system as a resource, how much entanglement can we broadcast so that resource still remains NPT. We have chosen $$3 \otimes 3$$ system as a first step to understand broadcasting of NPT states in higher dimensional systems. In particular, we find out the range of broadcasting of NPT entanglement for two-parameter class of states (TPCS) and isotropic states (IS). Interestingly, as a derivative of this process we are also able to locate the existence of absolute PPT (ABPPT) states in $$3 \otimes 3$$ dimensional system. Here we implement the strategy of broadcasting through approximate cloning operations.
Formally verified complete quantifier instatiation strategy for the theory of bounded linear integer arithmetic

The Isabelle/HOL proof assistant provides quite advanced and reliable support for discharging proof goals with external SMT solvers by reconstructing the resulting proof tree within the Isabelle/Pure inference kernel. In particular, currently Isabelle fully and efficiently supports proof reconstruction for the quantifier-free fragments of the theories of equality and uninterpreted functions (QF_UF) and linear integer arithmetic (QF_LIA). Yet there are a number of practically relevant theories that can be decided using relatively efficient algorithms, but are not supported either by the Isabelle proof reconstruction procedure or by the SMT solvers themselves. Meanwhile, if the decision procedure for an extended logical theory can be expressed as a complete quantifier instantiation strategy that reduces the given decision problem into the supported logical fragment (QF_UFLIA), the theory can be fully supported within the existing Isabelle/HOL infrastructure with relatively little additional effort. The goal of our work was to develop a fully formal completeness proof for an efficient quantifier instantiation strategy that translates a satisfiability problem in the logic of bounded linear integer arithmetic (we call it QF_BLIA) into the equisatisfiable problem in the logic of linear integer arithmetic and uninterpreted functions (QF_UFLIA). In this paper we first give the initial informal presentation of the proof, then discuss the most relevant issues of our resulting corresponding formal development of the completeness proof within Isabelle/HOL. The resulting formal proofs tightly follow the provided informal presentation, but at the same time they also contain quite significant amount of auxiliary developments that can potentially be generalized for other similar proof formalizations and automated using theory generation facilities of Isabelle/ML.
Il Boccaccio di Baldassar Castiglione: la duplice immagine del Certaldese nelle pagine del Cortegiano

Giovanni Boccaccio is quoted several times in Castiglione’s Cortegiano, but all these mentions are inserted in two specific contexts: on the one hand, the debate on literary language, developed in the letter of dedication to don Michel De Silva and in the first book; on the other hand, the definition of the joke in book II. Starting from these premises, this essay analyses the meanings of Boccaccio’s presence in the Cortegiano. It shows that Castiglione’s treatise provides two different and concurrent representations of the author of the Decameron: a positive one, connected to the ‘questione della lingua’, that offers Boccaccio as a promoter of the usage (‘uso’); a critical one, deriving from the theory of the ‘facezia’, that makes Boccaccio a challenging and challenged model.
Analysis of the Application of Machine Learning in Automatic Control Systems

In the modern world, automation affects almost all possible areas of human work. Currently, machine learning and neural networks attract a lot of attention in the technical field. In this regard, systems for automatic monitoring and analysis of automated systems are widely used. Machine learning and neural networks can significantly improve the efficiency of monitoring equipment, detecting product defects, and managing various parameters. The article discusses and analyzes the use of machine learning in such automatic control and monitoring systems. Various automatic control systems, monitoring the condition of equipment, testing the electrical parameters of electronic components, and detecting product defects are considered. Possible positive and negative aspects of applying machine learning to them are analyzed.
Two-subnet fusion for perceptual quality driven based underwater image enhancement

In recent years, enhancement of underwater images based on generative adversarial network (GAN) has been widely used. Aiming at the shortcoming that the texture details of the images generated by GAN are not clear enough, an underwater image pre-process unit and a generator including a perceptual subnet and a refine subnet are designed to improve visual quality of underwater images. The role of the perceptual subnet is to maintain the structure and semantic information of the input image. Refine subnet is to make the generated image clearer in the details of the texture. Specially, considering that underwater images may prevent the network from learning effectively, an underwater image pre-process unit is proposed to improve the quality of underwater images. To maintain the structure and semantic information of underwater images and preserve texture details of underwater images, the perceptual subnet and the refine subnet are proposed, respectively. Then, color-structure perception loss is proposed to obtain good performance in color and structure, and content loss and detail loss are proposed to keep content consistent and sharper texture. Ablation study verify the rationality and effectiveness of each loss function. Finally, subjective and objective experiments prove that the proposed method can obtain underwater images with higher visual quality and clearer texture details.