Reviewerly/exHarmony huggingface dataset:
A Framework to Detect Digital Text Using Android Based Smartphone

In this technology 2.0, there is a huge demand for storing knowledge available in paper documents format into a hardware disk. Text recognition application is an application of this modern technology era that attempts to develop an automatic data processing (ADP) system with the flexibility to automatically read the text from images. Using this application, one can reuse this information by the searching process. One simple method to store information from these paper documents into a computer system is to first scan the paper documents and to store them as images. But to reuse this information, it's totally difficult to read the individual contents and searching the contents form these documents line-by-line and word-by-word. The challenges involved during this are the font characteristics of the characters in the paper documents and standard of the images. To overcome these challenges, there is a need of an application called text recognition application. In this paper, we have discussed about the text recognition application which uses the recognition mechanisms to convert the documents in paper format to electronic format. For this, we are using android studio as a tool to develop an android based application to extract the text from images. The objective of this paper is to help others to get rid of their difficulties by making the process smooth. The application which we made will help students to copy their missed E-documents, assignments and laboratory records while submitting all in time. By this, filling up the form will become easier also. Along with this, we can make our on line bill payment mechanism and digital on line payment system smoother.
Impact of Climate Change in Rajshahi City Based on Marksim Weather Generator, Temperature Projections

Climate change is an increasingly important issue affecting natural resources. Rising temperatures, reductions in snow cover, and variability in precipitation depths and intensities are altering the accepted normal approaches for predicting runoff, soil erosion, and chemical losses from upland areas and watersheds. It is, therefore, essential to comprehend the future possible scenario of climate change in terms of global warming. Previous standards for natural resources planning may no longer apply in the near future, if local weather varies greatly from long-term historical records. General Circulation Models (GCMs) operate at large scales to estimate changes in atmospheric conditions (temperatures, wind speeds, precipitation, etc.) due to projected increase in greenhouse gas concentration in the atmosphere. The project described in this paper utilized the MarkSim DSSAT Weather Generator, Google Earth and a Microsoft Excel Spreadsheet. In this study, future impact of climate change on temperature of Rajshahi city in the year of 2030, 2050, and 2080 have been assessed based on four RCP scenarios (2.6, 4.5, 6.0, and 8.5) and ensemble 17 GCM models of a new MarkSim web version for IPCC AR5 data (CMIP5). As a result of multi-model combinations (RCPs and GCM projections) of future average temperature changes in 2030, 2050, and 2080 with respect to present climate condition, it has been observed that the winter months in Rajshahi city might become warmer in future at a faster rate than other months, and the trend of temperature increase might continue on a monthly basis in future.
Ultimate Reconstruction: Understand Your Bones From Orthogonal Views

3D image reconstruction is a common basis of medical image analysis, which requires a sequence of 2D slices/tomograms obtained from the relative motion to provide enough 3D information. When considering only the task to localize exception objects, a pair of two-view perspective 2D images may also be able to provide enough 3D information, which, however, has not been well studied. In this paper, we proposed the concept of Ultimate Reconstruction (UR) that reconstructs a 3D image from only a pair of two-view perspective 2D images. We resort techniques of generative adversarial network (GAN) to deal with this task, where we propose the Sense-consistency GAN (SGAN) with the sense-consistency constraint to learning the potential coarse-to-fine sense information during training the generative model. Experiments on the KiTS19 dataset with 300 subjects demonstrate that our SGAN achieves MAE/SSIM / PSNR values of 11.16% / 66.50%/23.82 when using only two 2D perspective images. It supports the possibility of UR and indicates that SGAN is promising to deal with UR.
Multi-Object Dynamic Memory Network For Cell Tracking in Time-Lapse Microscopy Images

Cell tracking in time-lapse microscopy images is important to study biological processes. We propose a new multi-object tracking method based on a dynamic memory network and template matching. Cells are detected by a fully convolutional neural network, and multiple dynamic memory units are used to track cells in successive frames. The template is dynamically updated using a long-short term memory with attention to cope with changing cell appearance. Our method includes a motion constraint based on cell motion statistics to improve the robustness. To handle cell mitosis events, a deep mitosis detector is integrated in our tracking method. We evaluated the proposed method on time-lapse microscopy datasets including data from the Cell Tracking Challenge. Experimental results demonstrate that our method yields state-of-the-art results or better results than baseline methods.
Boosting Photonic Quantum Computation with Moderate Nonlinearity

Photonic measurement-based quantum computation (MBQC) is a promising route towards fault-tolerant universal quantum computing. A central challenge in this effort is the huge overhead in the resources required for the construction of large photonic clusters using probabilistic linear-optics gates. Although strong single-photon nonlinearity ideally enables deterministic construction of such clusters, it is challenging to realise in a scalable way. Here we explore the prospects of using moderate nonlinearity (with conditional phase shifts smaller than $\pi$) to boost photonic quantum computing and significantly reduce its resources overhead. The key element in our scheme is a nonlinear router that preferentially directs photonic wavepackets to different output ports depending on their intensity. As a relevant example, we analyze the nonlinearity provided by Rydberg blockade in atomic ensembles, in which the trade-off between the nonlinearity and the accompanying loss is well understood. We present protocols for efficient Bell measurement and GHZ-state preparation -- both key elements in the construction of cluster states, as well as for the CNOT gate and quantum factorization. Given the large number of entangling operations involved in fault-tolerant MBQC, the increase in success probability provided by our protocols already at moderate nonlinearity can result in a dramatic reduction in the required resources.
Wisdom Of The Crowd For Early Detection In Barrett’s Esophagus

Cell detection is an essential task for characterizing and studying tumor microenvironments (TME). Automatic cell detection in histopathology is challenging due to the diversity of cell shape, size, morphology, as well as stain variations between laboratories. Though deep learning has become the choice method to tackle this task, typically training requires a large number of annotations, which can be laborious and time consuming. While recent developments to tackle this annotation problem have seen success, typically these pipelines add complexity to training and may not be easy to implement. In this paper we demonstrate that using several public datasets we can train a competitive cell detection network for Barrett's Esophagus (BE) premalignant tissue samples using conventional supervised training methods. To adapt the network to clinical BE tissue sections, pseudolabels were generated to retrain the network. The results indicate that public cell detection datasets can be used to train networks that generalize well to pre-cancer tissue samples without requiring any manual annotations, which can accelerate digital pathology research for early detection.
Geometric Morphology Based Irrelevant Vessels Removal For Accurate Coronary Artery Segmentation

Accurate semantic segmentation of coronary artery for CT images is critical in both coronary-related disease diagnosis (e.g., stenosis detection and plaque grading) and further intervention treatments. Considering the irrelevant tubular structures are usually difficult to be distinguished from the coronary arteries, e.g., veins, existing methods inevitably lead to false positives. In this paper, we incorporate the voxel and point cloud based segmentation methods into a coarse-to-fine framework for accurate coronary artery segmentation from Coronary Computed Tomography Angiography (CCTA) images. Specifically, after the coarse segmentation from any appealing voxel-based framework, initial segmentation maps are converted into point clouds and fed into a Refinement Module to filter out the irrelevant tubular vessels. In practice, the Refinement Module adopts the local feature aggregation on point clouds for contextual learning, capturing the geometric morphology of the coronary arteries. Furthermore, the first annotated CCTA dataset for coronary artery segmentation, named CORONARY-48 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , is released in this paper. Extensive experiments indicate that the proposed approach achieves state-of-the-art performance in coronary artery segmentation, improving the dice metric by 10% and preserving its fine structure as well.
VIKING: Adversarial Attack on Network Embeddings via Supervised Network Poisoning

Learning low-level node embeddings using techniques from network representation learning is useful for solving downstream tasks such as node classification and link prediction. An important consideration in such applications is the robustness of the embedding algorithms against adversarial attacks, which can be examined by performing perturbation on the original network. An efficient perturbation technique can degrade the performance of network embeddings on downstream tasks. In this paper, we study network embedding algorithms from an adversarial point of view and observe the effect of poisoning the network on downstream tasks. We propose VIKING, a supervised network poisoning strategy that outperforms the state-of-the-art poisoning methods by up to \(18\%\) on the original network structure. We also extend VIKING to a semi-supervised attack setting and show that it is comparable to its supervised counterpart.
Accurate 3d Kidney Segmentation Using Unsupervised Domain Translation And Adversarial Networks

Computed tomography urography imaging is routinely performed to evaluate the kidneys. Kidney 3D segmentation and reconstruction from urographic images provides physicians with an intuitive visualization way to accurately diagnose and treat kidney diseases, particularly used in surgical planning and outcome analysis before and after kidney surgery. While 3D fully convolution networks have achieved a big success in medical image segmentation, they get trapped in clinical unseen data and cannot be adapted in deferent modalities with one training procedure. This study proposes an unsupervised domain adaptation or translation method with 2D networks to deeply learn urographic images for accurate kidney segmentation. We tested our proposed method on clinical urography data. The experimental results demonstrate our proposed method can resolve the domain shift problem of kidney segmentation and achieve the comparable or better results than supervised learning based segmentation methods.
No Time to Hash: On Super-Efficient Entropy Accumulation

Real-world random number generators (RNGs) cannot afford to use (slow) cryptographic hashing every time they refresh their state R with a new entropic input X. Instead, they use "superefficient" simple entropy-accumulation procedures, such as $$ R \leftarrow \mathsf {rot}_{\alpha , n}(R) \oplus X, $$ where $$\mathsf {rot}_{\alpha ,n}$$ rotates an n-bit state R by some fixed number $$\alpha $$ . For example, Microsoft's RNG uses $$\alpha =5$$ for $$n=32$$ and $$\alpha =19$$ for $$n=64$$ . Where do these numbers come from? Are they good choices? Should rotation be replaced by a better permutation $$\pi $$ of the input bits? In this work we initiate a rigorous study of these pragmatic questions, by modeling the sequence of successive entropic inputs $$X_1,X_2,\ldots $$ as independent (but otherwise adversarial) samples from some natural distribution family $$\mathcal{D}$$ . Our contribution is as follows.
A Deeper Analysis of AOI Coverage in Code Reading

The proportion of areas of interest that are covered with gaze is employed as metric to compare natural-language text and source code reading, as well as novice and expert programmers’ code reading behavior. Two levels of abstraction are considered for AOIs: lines and elements. AOI coverage is significantly higher on natural-language text than on code, so a detailed account is provided on the areas that are skipped. Between novice and expert programmers, the overall AOI coverage is comparable. However, segmenting the stimuli into meaningful components revealed that they distribute their gaze differently and partly look at different AOIs. Thus, while programming expertise does not strongly influence AOI coverage quantitatively, it does so qualitatively.
Characterizing The Uncertainty Of Label Noise In Systematic Ultrasound-Guided Prostate Biopsy

Ultrasound imaging is a common tool used in prostate biopsy. The challenges associated with using a systematic and nontargeted approach are the high rate of false negatives and not being patient specific. Intraprostatic pathology information of individuals is not available during the biopsy procedure. Even after histopathology analysis of the biopsy cores, the report only represents a statistical distribution of cancer within the core. Labeling the data based on these noisy labels results in challenges for network training, where networks inevitably overfit to noisy data. To overcome this problem, we argue that it is critical to build a clean dataset. In this paper, we address the challenges associated with using statistical labels and alleviate this issue by taking advantage of confident learning to estimate uncertainty in the data label. Next, we find the label error, clean the labels, and evaluate the clean data by comparing it using a metric based on the involvement of cancer in core.
A Machine Learning Approach for the Land-Type Classification

Land-type classification is an essential aspect of resource planning, and correctly classifying a land type can play a crucial role in designing and executing efficient utilization of land types for agriculture and other purposes. In recent years, machine learning (ML) methods have become popular, shown significant improvements in their performance and have been applied in various domains. In this work, we have proposed an ML-based approach for efficient and accurate classification of land types. We extensively experimented with different ML methods such as decision tree (DT), support vector machine (SVM), random forests (RF) and K-nearest neighbour (KNN). The empirical results suggest that ML-based approaches are superior for land-type classification. It is also found that out of the different ML methods applied on Statlog Landsat dataset, SVM outperforms other methods and achieves 92% accuracy better than other state-of-the-art methods.
MIC: Multi-view Image Classifier using Generative Adversarial Networks for Missing Data Imputation

In this paper, we propose a framework for image classification tasks, named MIC, that takes as input multi-view images, such as RGB-T images for surveillance purposes. We combine auto-encoder and generative adversarial network architectures to ensure the multi-view embedding in a common latent space. Then, the resulting features are fed to the classification stage. The proposed framework is able to, all at once, train the multi-view embedding model to find a shared latent representation for the different views, perform data imputation (generate the missing views) and ensure the classification task by predicting the labels. Experiments on the MNIST dataset with a panoply of classifiers and several missingness ratios show the effectiveness of our solution.
Tree Search Techniques for Adversarial Target Tracking With Distance-Dependent Measurement Noise

We study the problem of devising a closed-loop strategy to control the position of a robot that is tracking a possibly moving target. The robot obtains noisy measurements of the target’s position. The measurement noise depends on the relative states of the robot and the target. We consider scenarios where the measurement values are chosen by an adversary, so as to maximize the estimation error. Furthermore, the target may be actively evading the robot. Our main contribution is to devise a closed-loop control policy for distance-dependent target tracking that plans for a sequence of control actions, instead of acting greedily. We consider a game-theoretic formulation of the problem and seek to minimize the maximum uncertainty (trace of the posterior covariance matrix) over all possible measurement values. We exploit the structural properties of a Kalman Filter to build a policy tree that is orders of magnitude smaller than naive enumeration while still preserving optimality guarantees. We show how to obtain even more computational savings by relaxing the optimality guarantees. The resulting algorithms are evaluated through simulations and experiments with real robots.
Efficient quantum key distribution based on hybrid degrees of freedom

Round-robin differential-phase-shift (RRDPS) quantum key distribution (QKD) protocol is a practical QKD protocol without monitoring the signal disturbance. However, how to further increase the secure key rate is still a challenge in practice. In the paper, we propose a high efficient QKD protocol based on RRDPS-QKD, where the photon-states are encoded in two degrees of freedom (DOFs), phase and polarization. The key generation rate and the security of the proposal protocol are analyzed. The simulation results show that the secure key rate is greatly improved in comparison with the original RRDPS-QKD protocol by breaking the limitation of quantum state in a single DOF. Moreover, our protocol can still maintain the high error tolerance of RRDPS-QKD protocol.
Assessment of composite materials on encrypted secret message in image steganography using RSA algorithm

The use of the internet in this modern era is increased many fold. The communications between different peers take place in digital form. While sharing the messages between different recipients, the confidentiality of the messages is very important. For creating the high level of security while sharing the secret messages, the cryptographic algorithms are introduced along with steganography. Image Steganography is a methodology used to hide the messages inside of the cover image. Initially, the secret information is encrypted by using the RSA Algorithm. Then the encrypted secret information is hidden in the Least Significant Bit (LSB) of the different components of the color image in such a way that the original quality of the image to be minimized. The recipient of the message is able to retrieve the encrypted secret message from the LSB bit of stego_image and then the cipher text is converted into original plain text by using the RSA algorithm. The proposed algorithm verified and analysed its performance against the different combinations of key pairs.
High‐quality steganography scheme using hybrid edge detector and Vernam algorithm based on hybrid fuzzy neural network

Summary There has been a developing extension in the utilization of steganography, because of the advancement in utilizing web innovation and interactive media innovation. Subsequently, these days, the data are not made sure about adequately while transmitting it over the system. Data are the most significant resource for the current electronic communication framework. Thus the data security has played a significant job to give protection from unapproved people. Steganography is the technique that provides the solution for this challenge. This article proposes a novel type of steganography strategy in which it uses the hybrid algorithm for higher security and a hybrid fuzzy neural network is utilized for the good quality of stego images. For higher security, this article utilizes the Sobel and Kirsch edge identifier and then the Vernam algorithm utilized for encrypting the secret image. Thereafter this article utilizes the combination of fuzzy logic and neural network for enhancing the quality of stego images. There are various advantages of this technique that it can be utilized in various areas, for example, confidential data communication in the defense sector, medical, electronic governance, banking, communication, and so on.
Relationship Between Fraud Auditing and Forensic Accounting

The duties, responsibilities and expectations of accountants and auditors have become clearer today. A person’s knowledge of accounting, auditing, law and modern technology may be required to find solutions to some disputes encountered in both private and business lives. This need arises in the areas that constitute the intersection of law and accounting, especially fraud auditing. This situation has led to the emergence of forensic accountancy/forensic auditorship, which is a separate recognized area of expertise. The aim of this study is to uncover the relationship between forensic accountancy or forensic consultancy or their areas of expertise with fraud and fraud auditing. Stakeholders in need of forensic accountants are also diverse. These may include Lawyers, police and law enforcement agencies, insurance companies, banks, courts, business world, government representatives and government legal entities. The main difference between forensic accounting and classical accounting is that the former investigate and reveal accounting frauds with a systematic approach. Auditing, fraud auditing and forensic accounting are closely related fields. Auditing knowledge and expertise will be useful in fraud auditing and forensic accounting. Similarly, mastering the technical and scientific aspects of accounting will also have a positive effect on fraud auditing. The three main fields of activity of forensic accountancy are fraud auditing or investigative accountancy (administrative support), litigation support (legal support) and expert witnessing (expert testimony). It is possible to benefit from many practices such as techniques applied by the independent auditor in fraud auditing, planning, applied procedures, fraud risk indicators and documentation in forensic accounting studies. In most developed countries, there are organizations providing services in the field of forensic accounting and fraud auditing, and various titles given to them. Comparatively, even in areas not directly related to forensic accounting practice in Turkey, competent authorities to make arrangements in this area. Additionally, there are various regulations regarding forensic accounting profession in the legal system.