Reviewerly/exHarmony huggingface dataset:
Image Enhancement and Denoising in Extreme Low-Light Conditions

Image noise refers to the specks of false colors or artifacts that diminish the visual quality of the captured image. It has become our daily experience that with affordable smart-phone cameras we can capture high clarity photos in a brightly illuminated scene. But using the same camera in a poorly lit environment with high ISO settings results in images that are noisy with irrelevant specks of colors. Noise removal and contrast enhancement in images have been extensively studied by researchers over the past few decades. But most of these techniques fail to perform satisfactorily if the images are captured in an extremely dark environment. In recent years, computer vision researchers have started developing neural network-based algorithms to perform automated de-noising of images captured in a low-light environment. Although these methods are reasonably successful in providing the desired de-noised image, the transformation operation tends to distort the structure of the image contents to a certain extent. We propose an improved algorithm for image enhancement and de-noising using the camera’s raw image data by employing a deep U-Net generator. The network is trained in an end-to-end manner on a large training set with suitable loss functions. To preserve the image content structures at a higher resolution compared to the existing approaches, we make use of an edge loss term in addition to PSNR loss and structural similarity loss during the training phase. Qualitative and quantitative results in terms of PSNR and SSIM values emphasize the effectiveness of our approach.
Open Curvature Scale Space Matching for Coronary Artery Identification in X-Ray Angiographic Images

Identification of the coronary artery in sequence angiograms is an important issue in the analysis and treatment of coronary angiography data, due to the ambiguities in the coronary vessels resulting from cardiac motion and the differences in the projection angles in angiograms. In this paper, we propose a novel coronary artery identification method based on curvature scale space image matching. First, points on the centerline of each coronary artery branch are convolved with Gaussian kernel functions of different scales to obtain the corresponding curvature scale space (CSS) image during the curve evolution. Then, a CSS image matching method for open curves, which uses a combination of open feature vectors and closed feature vectors, is proposed. Finally, curve matching transitivity is used to solve the problem of the inability to recognize the coronary artery when the difference in the imaging angles is too large. The experimental results demonstrate that the proposed method can recognize every coronary artery in different views, even when the difference in the imaging angles is 29.8°.
An Extended CTRT for AES-256

At CRYPTO 2000, Desai proposed a simple and faster AONT based on the CTR mode of encryption (called, CTRT) and proved its security in the ideal cipher model. Though AES-128 whose key length $$k=128$$ and block length $$l=128$$ can be used in CTRT as a block cipher, AES-256 cannot be used in CTRT due to its intrinsic restriction of $$k \le l$$. According to a recent ECRYPT-CSA report, AES-256 is strongly recommended rather than AES-128 for long term protection (security for thirty to fifty years) and post-quantum security. In this paper, we propose an extended CTRT (named as XCTRT) suitable for AES-256. By thoroughly evaluating all the tricky cases, we prove that XCTRT is secure in the ideal cipher model under the same AONT security definition of Desai. Also, we discuss the security result of XCTRT in concrete parameter settings. After showing performance measurements of XCTRT, we can say that our XCTRT has high speed encoding/decoding performance and is quite practical to be deployed in the real-world applications (e.g., cloud storage service).
Infrastructure Requirements for Cybersecurity

The aim of this study is to create a model to meet the global infrastructure requirements of cybersecurity. The general framework of the study was determined by examining the cybersecurity infrastructures used around the world and the improvements needed by these infrastructures, and by synthesising the research on how to provide an effective solution with an innovative model.
Modelling Patient Sequences for Rare Disease Detection with Semi-supervised Generative Adversarial Nets

Rare diseases affect 350 million patients worldwide, but they are commonly delayed in diagnosis or misdiagnosed. The problem of detecting rare disease faces two main challenges: the first being extreme imbalance of data and the second being finding the appropriate features. In this paper, we propose to address the problems by using semi-supervised generative adversarial networks (GANs) to deal with the data imbalance issue and recurrent neural networks (RNNs) to directly model patient sequences. We experimented with detecting patients with a particular rare disease (exocrine pancreatic insufficiency, EPI). The dataset includes 1.8 million patients with 29,149 patients being positive, from a large longitudinal study using 7 years medical claims. Our model achieved 0.56 PR-AUC and outperformed benchmark models in terms of precision and recall.
PRACTICING DISCUSSION IN THE FORM OF PYRAMID TO IMPROVE STUDENTS’ SPEAKING PERFORMANCE AND CLASSROOM INTERACTION

Speaking is an ability to use the language in oral form to express what is on language learners’ thinking. Learners have speaking skills if they have skills to practice grammar, comprehension, vocabulary, pronunciation, and fluency in the oral form. To attain the speaking skills, English teachers are encouraged to meet an appropriate learning strategy to be applied in the speaking classes. This study is a collaborative classroom action research. It is aimed at describing how the use of pyramid discussion to enhance students’ speaking performance and classroom interaction. Its steps consisted of Planning, Acting, Observing, and Reflecting. This research was conducted at the 8th-grade students of SMPN 1 Praya Timur, Central Lombok. There were three instruments employed to obtain the data namely test, observation sheet, and questionnaire. After having the cycle 1, the findings showed that there were 16 students who got score more than 70. The calculation of the mean score was 77.30. Meanwhile, the tabulation of the percentage was 88.90. The data of the observation sheet indicated that the students were enthusiastic in classroom interaction and they feel interested in learning English. The research findings showed that the mean score was 83.22. It could be concluded that the use of pyramid discussion was able to improve students’ speaking performance and classroom interaction at the 8th-grade students of SMPN 1 Praya Timur, Central Lombok.
Meta-analysis in applied linguistics

This chapter discusses the application of meta-analysis, a research synthesis method that synthesizes quantitative studies to analyze data from multiple studies in applied linguistics. As the field of applied linguistics matures and the number of studies in the field increases, there is a growing need to summarize previous, relevant studies to show what is known and what is not yet known and therefore warrants further research. This is of crucial importance for researchers in understanding the overall findings and characteristics of applied linguistics studies and in designing new studies based on the accumulated knowledge in the field. This is equally important for applied linguistics as a field, because each study, when combined, characterizes the field and influences its future development. In this chapter, we describe a method of meta-analysis that synthesizes quantitative studies. Meta-analysis is useful not only in synthesizing studies and indicating the overall effects but also in terms of identifying the sources of inconsistent findings across studies. We describe how meta-analysis can be used to analyze applied linguistics data from multiple studies. We also discuss the limitations of the method and the potential applications of the method to future applied linguistics studies.
Efficient Multi-Domain Dictionary Learning With GANS

In this paper, we propose the multi-domain dictionary learning (MDDL) to make dictionary learning-based classification more robust to data representing in different domains. We use adversarial neural networks to generate data in different styles, and collect all the generated data into a miscellaneous dictionary. To tackle the dictionary learning with many samples, we compute the weighting matrix that compress the miscellaneous dictionary from multi-sample per class to single sample per class. We show that the time complexity solving the proposed MDDL with weighting matrix is the same as solving the dictionary with single sample per class. Moreover, since the weighting matrix could help the solver rely more on the training data, which possibly lie in the same domain with the testing data, the classification could be more accurate.
Semantic Adversarial Perturbations using Learnt Representations

Adversarial examples for image classifiers are typically created by searchingfor a suitable norm-constrained perturbation to the pixels of an image.However, such perturbations represent only a small and rather contrived subsetof possible adversarial inputs; robustness to norm-constrained pixelperturbations alone is insufficient. We introduce a novel method for theconstruction of a rich new class of semantic adversarial examples. Leveragingthe hierarchical feature representations learnt by generative models, ourprocedure makes adversarial but realistic changes at different levels ofsemantic granularity. Unlike prior work, this is not an ad-hoc algorithmtargeting a fixed category of semantic property. For instance, our approachperturbs the pose, location, size, shape, colour and texture of the objects inan image without manual encoding of these concepts. We demonstrate this newattack by creating semantic adversarial examples that fool state-of-the-artclassifiers on the MNIST and ImageNet datasets.
Image selection based on low level properties for lifelog moment retrieval

The increasing number of mobile and wearable devices is dramatically changing the way we collect data about person’s life. These devices allow recording our daily activities and behavior in several forms, e.g., text, images, bio-signals, or video. However, many times, the collected data includes low quality or irrelevant contents, feeding lifelogging applications with huge amounts of data, and creating computational challenges for patterns’ identification. In this paper, we propose a fast image analysis approach to automatically select relevant images from lifelog data. Using images intrinsic information, such as scenes and objects, we have manually curated two datasets, one with relevant content and another with non-relevant information. Then, we applied supervised learning algorithms based on low-level image features, namely blur and focus, to find the binary model that best discriminates between the two classes. The binary models were then compared based on learning curves and f1-scores, achieving a 95.4% of f1-score for the best one. By reducing the amount of images in the lifelog data, we were able to save computational time without losing images with relevant content.
Using Cuda and Blockchain Technologies to Recover an Encrypted Pdf File Password

The article presents an algorithm for using parallel computing technology using CUDA and blockchain technology to recover the password for an encrypted PDF file. The analysis and comparison of the computational speed using a graphics processor (GPU) and a central processing unit (CPU) are carried out. The testing of the implemented algorithm is carried out. A statistical analysis of the results of experimental studies is presented.
Bacterial foraging optimization algorithm with mutation to solve constrained problems

A simple version of a Swarm Intelligence algorithm called bacterial foraging optimization algorithm with mutation and dynamic stepsize (BFOAM-DS) is proposed. The bacterial foraging algorithm has the ability to explore and exploit the search space through its chemotactic operator. However, premature convergence is a disadvantage. This proposal uses a mutation operator in a swim, similar to evolutionary algorithms, combined with a dynamic stepsize operator to improve its performance and allows a better balance between the exploration and exploitation of the search space. BFOAM-DS was tested in three well-known engineering design optimization problems. Results were analyzed with basic statistics and common measures for nature-inspired constrained optimization problems to evaluate the behavior of the swim with a mutation operator and the dynamic stepsize operator. Results were compared against a previous version of the proposed algorithm to conclude that BFOAM-DS is competitive and better than a previous version of the algorithm.
RESEARCH PROGRESS ON FEATURE MATCHING OF SAR AND OPTICAL IMAGES

Abstract. Before obtaining information and identifying ground target from images, image matching is necessary. However, problems of strong pixel noise interference and nonlinear gray scale differences in synthetic aperture radar image still exist. Feature matching becomes a kind possible solution. To learn the research progress of SAR and optical image matching, as well as finding solutions for above matching problems, a summary for feature matching with SAR and optical image is indispensable. By listing three typical methods below, we can discuss and compare how researchers improve and innovate methods for feature matching from different angles in matching process. First method is feature matching method proposed by CHEN Min et. It uses phase congruency method to detect point features. Feature descriptors are based on gaussian-gamma-shaped edge strength maps instead of original images. This method combines both edge features and point features to reach a match target. The second one is SAR-SIFT algorithm of F. Dellinger et. This kind of method is based on improvement of sift algorithm. It proposes a SAR-Harris method and also a calculation method for features descriptors named gradient by ratio. Thirdly, it is feature matching method proposed by Yu Qiuze et. By using edge features of image and improvement of hausdorff distance for similarity measure, it applies genetic algorithm to accelerate matching search process to complete matching tasks. Those methods are implemented by using python programs, and are compared by some indexes. Experimental data used multiple sets of terrasar and optical image pairs of different resolutions. To some extent, the results demonstrate that all three kinds of feature methods can improve the matching effect between SAR and optical images. It can be easier to reach match purposes of SAR and optical images by using image edge features, while such methods are too dependent on the edge features.
Towards Understanding the Regularization of Adversarial Robustness on Neural Networks

The problem of adversarial examples has shown that modern Neural Network (NN) models could be rather fragile. Among the more established techniques to solve the problem, one is to require the model to be {\it $\epsilon$-adversarially robust} (AR); that is, to require the model not to change predicted labels when any given input examples are perturbed within a certain range. However, it is observed that such methods would lead to standard performance degradation, i.e., the degradation on natural examples. In this work, we study the degradation through the regularization perspective. We identify quantities from generalization analysis of NNs; with the identified quantities we empirically find that AR is achieved by regularizing/biasing NNs towards less confident solutions by making the changes in the feature space (induced by changes in the instance space) of most layers smoother uniformly in all directions; so to a certain extent, it prevents sudden change in prediction w.r.t. perturbations. However, the end result of such smoothing concentrates samples around decision boundaries, resulting in less confident solutions, and leads to worse standard performance. Our studies suggest that one might consider ways that build AR into NNs in a gentler way to avoid the problematic regularization.
The Visitors’ Perception Toward the Comfort of Social Interaction in Public Space ( A Case Study in Karebosi Field Makasar)

Public space has an important role to incresase physical health and social interaction of city communities. Karebosi field is public space which is in the development priority by Makasar city government. Karebosi field is the most ideal facility to represent the concept of an open public space in Makasar. After going through revitalization process, the government still continues to reform and refine Karebosi field functions. This research aims to find out the visitors’ perception towards the comfort of social interaction in Karebosi field, and what factors which are influence them as well. This research was conducted in 2018 at Karebosi field, Makasar. The method used in this research is mixed method (qualitative – quantitative). The technique of data collection is done by evidentiary method through data triangulation (observation, questionnaire, and interview). The result of this research can be concluded that the facility in Karebosi field is quite successful in attracting the visitors to do sport activities in public space, yet the level of success is still not optimal because it is still in the range of 60.89%, it is due to the facility development that have not been varied, the facilities are considered as not being able to stimulate the diversity of social interaction and increasing the number in public space. Designing, Karebosi field is not able to give impression and meaning for the people who do the activities in it. It is expected to be a reference for Makasar government in formulating the steps to increase the service quality and the functions of Krebosi field in the future.&#x0D; &#x0D;
Deep Groupwise Registration of MRI Using Deforming Autoencoders

Groupwise image registration and the estimation of anatomical shape variation play an important role for dealing with the analysis of large medical image datasets. In this work we adapt the concept of deforming autoencoders that decouples shape and appearance in an unsupervised learning setting, following a deformable template paradigm, and apply its capability for groupwise image alignment. We implement and evaluate this model for the application on medical image data and show its suitability for this domain by training it on middle slice MRI brain scans. Anatomical shape and appearance variation can be modeled by means of splitting a low-dimensional latent code into two parts that serve as inputs for separate appearance and shape decoder networks. We demonstrate the potential of deforming autoencoders to learn meaningful appearance and deformation representations of medical image data.
Sample Complexity of Representation Learning for Sparse and Related Data Models

This thesis investigates the sample complexity of representation learning algorithms for sparse and related signal models. Two frameworks are developed for establishing bounds on the generalization error and sample complexity for a general class of learned representation models based on dictionaries. The proposed bounding schemes are presented in a self-contained manner and can be used to analyze a wide variety of learning scenarios. Both methods are then applied to a selection of learning algorithms.
Super-Resolution via Wavelet Transform and Advanced Learning Techniques

Image super-resolution aims to generate a high-resolution (HR) image from a low-resolution (LR) input image. In this paper, we propose a deep learning-based approach for image super-resolution. We use the wavelet transform to separate the input image into four frequency bands, and train a model for each sub-band. By processing information from different frequency bands via different CNN models, we can extract features more efficiently and learn better LR-to-HR mapping. In addition, we add dense connection to the model to make better use of the internal features in the CNN model. Furthermore, geometric self-ensemble is applied in the testing stage to maximize the potential performance. Extensive experiments on four benchmark datasets show the efficiency of the proposed method.
Internet Banking Fraud Detection Using Deep Learning Based on Decision Tree and Multilayer Perceptron

Fraud transactions have become a growing problem in the online banking sphere. As technology progresses, fraudsters also change their methods of committing fraud. There are also emerging technologies that allow fraudsters to mimic the transaction behavior of genuine customers and they also keep changing their methods so that it is difficult to detect fraud. This paper discusses the importance of fraud detection methods and compares Hidden Markov Model, Deep Learning, and Neural Network that are used to detect fraud in online banking transactions.
A Method of Online Trajectory Generation Based on FIR Filter

In this paper, a method of smooth trajectory generation is suggested. In previous studies, Finite Impulse Response (FIR) filter is used to turn low computational cost acceleration-limited trajectory into jerk-limited trajectory. However, FIR filter prolongs the duration of acceleration so that trajectory overshoots the end point. To solve the problem, a novel method is proposed to make trajectory approach the end point without overshooting. Starting from an state with suitable initial velocity and acceleration, the proposed method can generate smooth and almost time-optimal trajectory and provide a new feasible trajectory in real-time.
Using lead isotope ratios to distinguish between samples of diffrent uranium mines

a bstract In this work, a total of twenty samples selected for investigation originate from South Africa and Namibia uranium mines. The aim of this study was to determine whether the lead ratios measured in particular samples could be used to attribute the uranium sample to the production or reprocessing plant. Measurements were carried out using an inductively coupled plasma mass spectrometer PerkinElmer NexION 2000. The precision obtained for the 208 Pb/ 206 Pb ratio, the 207 Pb/ 206 Pb ratio and the 204 Pb/ 206 Pb ratio values was considerably lower than 0.02% demonstrating the applicability of the technique for Pb isotope ratio studies.
Improving the Quality of Academic Services in Higher Education by the Development of SIMANTAP

Universities as educational providers need to provide good academic services for students.Academic services in the 4.0 era can be provided by creating an internet-based academic system.When a university does not have an internet-based academic service system, the university cannot provide good and quality academic services.This article aims at developing and analyzing academic services at universities by using an internet-based academic service system and Enterprise Architecture (EA).An academic service system called SIMANTAP (Sistem Informasi Manajemen Akademik Terpadu/Integrated Academic Management Information System) was made to implement in the academic service system at a university.The research method used was design-based research using the stages of defining, designing, developing, and reflecting.The research was conducted at four faculties at one university in West Java Province, Indonesia, which did not have an internet-based academic service system.The results showed that the utilization of academic service systems in universities has an important role in the process of conducting academic activities both for students, lecturers, and university staff.The academic activities were greatly helped by the use of internet-based academic service system.In its implementation, all users needed to know more and get used to using it, and the system designer needs to be responsive to anticipate any weaknesses in the system so that the academic service process runs well continuously.