Reviewerly/exHarmony huggingface dataset:
Image Enhancement of Historical Image Using Image Enhancement Technique

Image enhancement technique is one of the very challenging issues in the image processing technique. The main motive of the image enhancement process is to work on an image or picture so that the result should be more applicable than original. Historical docs are very important origin of information, but it commonly suffers from the problem of getting degrade. The main aim is to keep all of these documents secure and alive. The purpose is to enhance the quality and visualization of the historical image. In this paper, we have used improved adaptive histogram equalization of the contrast of the image, and we have analyzed the result.
AN EMPIRICAL STUDY OF CANCER CLASSIFICATION TECHNIQUES BASED ON THE NEURAL NETWORKS

Cancer is one of the most common dreadful diseases prevailing worldwide, and patients with cancer are rescued only when the cancer is detected at a very early stage. Early detection of cancer is appropriate as in the fourth stage, but the chance of survival is limited. The symptoms of cancers are rigorous, and therefore, all the symptoms should be studied properly before the diagnosis. Thus, an automatic prediction system is necessary for classifying the tumor, i.e. malignant or benign tumor. Over the past few years, cancer classification is increased rapidly, but there is no general technique to find novel cancer classes (class discovery) or to assign tumors to known classes. Accordingly, this survey analyzes distinct cancer classification techniques. Thus, this review article provides a detailed review of 50 research papers presenting the suggested cancer classification techniques, like Deep learning-based techniques, Neural network-based techniques, and Hybrid techniques. Moreover, an elaborative analysis and discussion are made based on the year of publication, utilized datasets, accuracy range, evaluation metrics, implementation tool, and adopted classification methods. Eventually, the research gaps and issues of various cancer classification schemes are presented for extending the researchers towards a better future scope.
Computing the full signature kernel as the solution of a Goursat problem

Recently there has been an increased interested in the development of kernel methods for sequential data. An inner product between the signatures of two paths can be shown to be a reproducing kernel and therefore suitable to be used in the context of data science. An efficient algorithm has been proposed to compute the signature kernel by truncating the two input signatures at a certain level, mainly focusing on the case of continuous paths of bounded variation. In this paper we show that the full (i.e. untruncated) signature kernel is the solution of a Goursat problem which can be efficiently computed by finite different schemes (python code can be found in this https URL). In practice, this result provides a kernel trick for computing the full signature kernel. Furthermore, we use a density argument to extend the previous analysis to the space of geometric rough paths, and prove using classical theory of integration of one-forms along rough paths that the full signature kernel solves a rough integral equation analogous to the PDE derived for the bounded variation case.
TrustFlow-X

This article addresses the challenges of memory safety in life-critical medical devices. Since the last decade, healthcare manufacturers have embraced the Internet of Things, pushing technological innovations to increase market share. Medical devices, including the most critical ones, tend to be increasingly connected to the Internet. Unfortunately, as critical devices often rely on unsafe programming languages such as C, they are no exception to memory safety issues. Given a memory vulnerability, a skillful attacker can take over a system and perform remote code execution. Combined with the fact that medical devices directly impact the safety of their users, a security vulnerability can lead to disastrous scenarios. To address this issue, this article presents TrustFlow-X, a novel hardware/software co-designed framework that provides efficient fine-grained control-flow integrity protection against memory-based attacks. The TrustFlow-X framework is composed of an LLVM-based compiler toolchain that generates a secure code. This secure code is then executed on an extended RISC-V processor that keeps track of sensitive data using a trusted memory. The obtained results show that the contribution is practical, providing a high level of trust in life-critical embedded systems.
Robust Fusion of Probability Maps

The fusion of probability maps is required when trying to analyse a collection of image labels or probability maps produced by several segmentation algorithms or human raters. The challenge is to weight properly the combination of maps in order to reflect the agreement among raters, the presence of outliers and the spatial uncertainty in the consensus. In this paper, we address several shortcomings of prior work in continuous label fusion. We introduce a novel approach to jointly estimate a reliable consensus map and assess the production of outliers and the confidence in each rater. Our probabilistic model is based on Student’s t-distributions allowing local estimates of raters’ performances. The introduction of bias and spatial priors leads to proper rater bias estimates and a control over the smoothness of the consensus map. Image intensity information is incorporated by geodesic distance transform for binary masks. Finally, we propose an approach to cluster raters based on variational boosting thus producing possibly several alternative consensus maps. Our approach was successfully tested on the MICCAI 2016 MS lesions dataset, on MR prostate delineations and on deep learning based segmentation predictions of lung nodules from the LIDC dataset.
Investigations of Shallow and Deep Learning Algorithms for Tumor Detection

With the increasing ability of the computer aided detection and diagnosis system, the exploration on the tumors have attained a breakthrough by decreasing the mortality rate. Radiologists perform manual identification for both the diagnosis and prognosis of tumors in the victims. The research on the diagnosis of the tumor have reached a huge milestone using both the shallow and the deep learning algorithms. In this paper, classification of mass lesions in breast cancer dataset has been performed, for studying the comparison of shallow learning algorithms with that of the deep learning algorithms. For shallow learning the texture and statistical features were extracted, used in the contemporary classification algorithms. Of all shallow learning algorithms, support vector machines (SVM) showed 90.6% accuracy, whereas among the deep learning algorithms, the global features are taken for consideration and VGG16 showed 92.6% accuracy. Thus, these deep learning models outperformed the shallow algorithms and have been a state-of-art algorithm for an accurate and automated breast tumor detection.
Overestimation of Variability in Ensembles of Line Orientation, Size, and Hue

Ensemble perception utilizes our visual system's natural abilities to extract summary statistics from sets of similar objects. Our perceptual system can accurately detect the mean of these ensembles. However, previous research has shown that our visual system is biased to overestimate variability. This bias to overestimate variability was stronger when the objects were more similar to each other. This prior research concerned variability of line orientations. We extended this work to explore whether this overestimation bias is a general phenomenon and therefore applies to other visual features as well. Using the method of adjustment, participants made judgments about the variability of line orientation, size, and hue in sets of ensembles (lines and circles). Participants viewed 9 target circles of various sizes presented one-at-a-time, then adjusted the sizes of five comparison circles presented simultaneously to match the variability in the target display. A similar task was used to assess how participants estimated variability in line orientation and hue. Participants overestimated variability, and this was true for all 3 features. Moreover, participants overestimated variability to a greater extent when there was less variability in the display for circle size (95% overestimation), line orientation (51% overestimation), and hue (155% overestimation) compared to high levels of variability (-11%, -10%, -3%, respectively). Understanding how we perceive variability in ensembles is essential when making judgments about critical information in medical images. For instance, how distributed calcifications are within a mammogram informs whether a radiologist diagnoses a patient with cancer. Further research will attempt to elucidate the mechanisms of ensemble perceptual and test the robustness of the bias to overestimate variability.
Use of Virtual Reality Technology for CANDU 6 Reactor Fuel Channel Operation Training

The CANDU (CANada Deuterium Uranium) 6 nuclear reactor core consists of 380 horizontal fuel channels that hold the fuel in the reactor core and allow for the expansion of the fuel channel components. Fuel channel assembly enables the heat transfer from the nuclear fuel to heavy water coolant. Heavy water coolant under elevated pressure ( $$\sim $$ 10 MPa), carries the heat generated from nuclear fission reaction to heat exchangers located outside the reactor core to generate steam that will turn the turbine to generate electricity. The fuel channel assembly also allows the fueling machine to perform online refueling of nuclear fuel and provide shielding protection to safeguard the nuclear workers while providing structural integrity during the regular operation of the nuclear reactor. The physical configuration and coolant flow path of the CANDU fuel channel assembly is very complex, and teaching and training associated with the fuel channel assembly employ pictures and computer simulations. However, these traditional approaches lack the immersion and interactivity that virtual reality can provide to realistically expose learners in a safe and controlled manner otherwise difficult to achieve in real life due to the dangers of radiation exposure. In this paper, we present the development of virtual reality (VR) fuel assembly prototype generated for teaching CANDU concepts. Our preliminary study focused on understanding face validity and usability perceptions regarding the virtual interactions. Preliminary feedback from content exports provided us with valuable insights on improvements regarding usability, fidelity, and gamification.
Application and Research of Artificial Intelligence in Digital Library

With the advent of the intelligent age, artificial intelligence has become the mainstream technology in the world, and artificial intelligence technology has established a solid research foundation for digital libraries. This paper discusses the significance and form of artificial intelligence technology in the book library application and the future development trend, in order to provide an important reference for the further development of the establishment of digital libraries. This article starts with the basic theory of text classification, expounds the theory of text classification system and the key technologies and main algorithms of classification, and compares and analyzes the theoretical basis, applicable conditions and scope, advantages and disadvantages of various methods. Aiming at the characteristics of digital library text resources and Chinese language, with the help of rough and good ability to deal with ambiguity and uncertainty and the excellent function approximation ability and fast learning ability of RBF neural network, a digital book based on Rough-RBFNN has been established. The library text automatic classification model is optimized and the neural network is optimized. Based on this, the digital library text automatic classification system is designed and implemented. The experimental research results show that artificial intelligence realizes the intelligentization of machinery and equipment. The library should rationally use artificial intelligence technology and combine the readers’ current reading habits to design reading promotion scenarios that can reflect the characteristics of the collection, so that digital reading promotion has a broader development space, to better play the role of leading reading for all.
What’s next for social media companies? The digital regulatory scene in Turkey during the COVID-19 pandemic

In the midst of the Coronavirus pandemic, on 9 April 2020, a draft bill was presented to fight against the spread of COVID-19 in Turkey. Eight articles were buried deep in the proposed legislation, which mostly included economic measures and aid packages, directly targeting any social media company that had a platform accessed by over one million users daily. Although the articles on social media were dropped from the parliamentary schedule on 14 April 2020 to make way for more urgent bills on the economy and health, the uncertainty regarding social media companies’ situation in the country remained. Then, on 29 July 2020, the new social media law, officially ‘The Law on Making Amendments to the Law on Regulation of Publications on the Internet and Combating Crimes Committed by Means of Such Publication’, numbered 7253 was adopted by the parliament. This article approaches this issue from the perspective of social media companies, specifically Facebook and Twitter, and analyses the post-Coronavirus digital scene and public policy attempts from the corporate point of view.
Automatically Customizing Static Analysis Tools to Coding Rules Really Followed by Developers

Automatic Static Analysis Tools (ASATs) detect coding rule violations, including mistakes and bad practices that frequently occur during programming. While ASATs are widely used in both OSS and industry, the developers do not resolve more than 80% of the detected violations. As one of the reasons, most ASATs users do not customize their ASATs to their projects after installation; the ASATs with the default configuration report many rule violations that confuse developers. To reduce the ratio of such uninteresting warning messages, we propose a method to customize ASATs according to the product source code automatically. Our fundamental hypothesis is: A software project has interesting ASAT rules that are consistent over time. Our method takes source code as input and generates an ASAT configuration. In particular, the method enables optional (i.e., disabled by default) rules that detected no violations on the version because developers are likely to follow the rules in future development. Our method also disables violated rules because developers were unlikely to follow them. To evaluate the method, we applied our method to 643 versions of four JavaScript projects. The generated configurations for all four projects increased the ASAT precision. They also increased recall for two projects. The result shows that our method helps developers to focus on their attractive rule violations. Our implementation of the proposed method is available at https://github.com/devreplay/linter-maintainer
Irony Detection in Non-English Tweets

Sentiment analysis is the interpretation and classification of emotions conveyed by text data. While there have been many attempts to classify the sentiment of a given text, there have been few models that can do the same when provided with non-English data exhibiting sarcasm or irony. This paper aims to compare various techniques of sarcasm detection and decide which method works the best for datasets of different sizes and types. The models have been tested on datasets of three different non-English languages - Arabic, French and a Hindi-English code-mix. None of the presented models are language-specific and can be run on data of any language. A comparison between a sub-word model, the usage of Term Frequency-Inverse Document Frequency (TF-IDF) and neural networks, a Long Short-Term Memory (LSTM) model and machine learning techniques such as Nearest Neighbors, Decision Tree, Random Forest, AdaBoost, Naive Bayes (NB), Support Vector Machine (SVM) Linear, SVM radial basis function (RBF), SVM Sigmoid has been performed. The output for each language and model has been evaluated based on their F1-score, accuracy, precision, and recall.
Causal Inference Theory with Information Dependency Models

Inferring the potential consequences of an unobserved event is a fundamental scientific question. To this end, Pearl's celebrated do-calculus provides a set of inference rules to derive an interventional probability from an observational one. In this framework, the primitive causal relations are encoded as functional dependencies in a Structural Causal Model (SCM), which are generally mapped into a Directed Acyclic Graph (DAG) in the absence of cycles. In this paper, by contrast, we capture causality without reference to graphs or functional dependencies, but with information fields and Witsenhausen's intrinsic model. The three rules of do-calculus reduce to a unique sufficient condition for conditional independence, the topological separation, which presents interesting theoretical and practical advantages over the d-separation. With this unique rule, we can deal with systems that cannot be represented with DAGs, for instance systems with cycles and/or 'spurious' edges. We treat an example that cannot be handled-to the extent of our knowledge-with the tools of the current literature. We also explain why, in the presence of cycles, the theory of causal inference might require different tools, depending on whether the random variables are discrete or continuous.
DevNet: Self-supervised Monocular Depth Learning via Density Volume Construction

Self-supervised depth learning from monocular images normally relies on the 2D pixel-wise photometric relation between temporally adjacent image frames. However, they neither fully exploit the 3D point-wise geometric correspondences, nor effectively tackle the ambiguities in the photometric warping caused by occlusions or illumination inconsistency. To address these problems, this work proposes Density Volume Construction Network (DevNet), a novel self-supervised monocular depth learning framework, that can consider 3D spatial information, and exploit stronger geometric constraints among adjacent camera frustums. Instead of directly regressing the pixel value from a single image, our DevNet divides the camera frustum into multiple parallel planes and predicts the pointwise occlusion probability density on each plane. The final depth map is generated by integrating the density along corresponding rays. During the training process, novel regularization strategies and loss functions are introduced to mitigate photometric ambiguities and overfitting. Without obviously enlarging model parameters size or running time, DevNet outperforms several representative baselines on both the KITTI-2015 outdoor dataset and NYU-V2 indoor dataset. In particular, the root-mean-square-deviation is reduced by around 4% with DevNet on both KITTI-2015 and NYU-V2 in the task of depth estimation.
Motion Planning for Fixed-Wing UAV Formations Flight With Velocity and Heading Rate Constraints

Motion planning is one of the core technologies in unmanned aerial vehicle (UAV) formation flight. This paper proposes a method to plan the formation paths for fixed-wing UAVs, which allows fixed-wing UAVs to fly safely with desired formation configuration. The kinematics of each fixed-wing UAV in the formation system is described by a non-linear model with saturated angular speed and limited linear speed. Then, in order to reduce the formation tracking error during the formation flight, the limit of the leader UAV's heading angle rate is discussed in detail based on the leader-follower framework, which fully considers the dynamic constraints of fixed-wing UAVs. Furthermore, based on the feasible set of the formation heading rate, the optimal control theory is used for fixed-wing UAVs to solve the motion planning problems. Finally, the results of the numerical simulations are presented and analyzed to verify the effectiveness of the theoretical results.
Face Image Quality Vector Assessment for Biometrics Applications

In this paper, we propose a multi-task convolutional neural network which produces an image quality vector for an input face image. This image quality vector contains the face quality score and the information about the nuisance factors (i.e., pose, illumination, blurriness and expression) that caused the predicted image quality score. Our multitask network utilizes a pretrained ResNet-50 as its stem. Employing different data augmentation techniques, we create a huge and diverse dataset. We ground truth this dataset and use it to fine-tune our multi-task neural network. Our Multi-task learning framework enables us to learn a shared and beneficial feature representation among the relevant tasks to achieve a better performance. Moreover, the proposed multi-task neural network provides useful information about the nuisance factors. Nuisance factors information is useful for applications like face image quality assessment during the automatic enrollment process where user's photo should comply with a standardized criteria. In this case, if the user upload a low-quality image which does not comply with a predefined standard, the system provides feedback about the nuisance factors associated with the captured image. Therefore, the user can resolve the problem, and upload a new image that can remedy the issue. Although an extensive research has been done on face image quality assessment, non of them have addressed face image quality vector effectively. To the best of our knowledge, our method is the first method that uses deep learning approach to generate a face image quality vector. The evaluation of results demonstrates that our method gets higher or comparable accuracy for face image quality assessment in comparison to the state-of-the-art methods and analyzes the input image to provide detailed information about the nuisance factors.
Exploiting Information From Native Data for Non-Native Automatic Pronunciation Assessment

This paper proposes an end-to-end pronunciation assessment method to exploit the adequate native data and reduce the need for non-native data costly to label. To obtain discriminative acoustic representations at the phoneme level, the pretrained wav2vec 2.0 is re-trained with connectionist temporal classification (CTC) loss for phoneme recognition using native data. These acoustic representations are fused with phoneme representations derived from a phoneme encoder to obtain final pronunciation scores. An efficient fusion mechanism aligns each phoneme with acoustic frames based on attention, where all blank frames recognized by the CTC-based phoneme recognition are masked. Finally, the whole network is optimized by a multi-task learning framework combining CTC loss and mean square error loss between predicted and human scores. Extensive experiments demonstrate that it outperforms previous baselines in the Pearson correlation coefficient even with much fewer labeled non-native data.