Reviewerly/exHarmony huggingface dataset:
A recent review and a taxonomy for hard and soft tissue visualization‐based mixed reality

Abstract Background Mixed reality (MR) visualization is gaining popularity in image‐guided surgery (IGS) systems, especially for hard and soft tissue surgeries. However, a few MR systems are implemented in real time. Some factors are limiting MR technology and creating a difficulty in setting up and evaluating the MR system in real environments. Some of these factors include: the end users are not considered, the limitations in the operating room, and the medical images are not fully unified into the operating interventions. Methodology The purpose of this article is to use Data, Visualization processing, and View (DVV) taxonomy to evaluate the current MR systems. DVV includes all the components required to be considered and validated for the MR used in hard and soft tissue surgeries. This taxonomy helps the developers and end users like researchers and surgeons to enhance MR system for the surgical field. Results We evaluated, validated, and verified the taxonomy based on system comparison, completeness, and acceptance criteria. Around 24 state‐of‐the‐art solutions that are picked relate to MR visualization, which is then used to demonstrate and validate this taxonomy. The results showed that most of the findings are evaluated and others are validated. Conclusion The DVV taxonomy acts as a great resource for MR visualization in IGS. State‐of‐the‐art solutions are classified, evaluated, validated, and verified to elaborate the process of MR visualization during surgery. The DVV taxonomy provides the benefits to the end users and future improvements in MR.
An Approach Towards Resisting Side-Channel Attacks for Secured Testing of Advanced Encryption Algorithm (AES) Cryptochip

Cryptography is used for securing sensitive information from unauthorized access. A cryptochip needs to be tested so as to ensure its functionality. Scan based testing is the most popular technique employed for testing purposes, however, such testing of the cryptochips can lead to the retrieval of the secret information stored inside them by exploiting the scan chain structure known as the scan based side channel attack. Thus, it becomes crucial to guarantee the security of the cryptochip while maintaining its testing capabilities. In this paper, we have proposed two schemes to securely test the AES cryptochip by inserting a certain number of XOR gates and a combination of XOR gates and NOT gates at random positions inside the scan chain structure of the AES crypto module. Our results show that, without affecting the testability of the AES cryptochip, our proposed schemes are successfully able to guard itself against scan based side channel attacks with minimal area overhead of just 0.02% and with the probability of finding the scan chain structure being 1/2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">128</sup> .
Towards Wireless Environment Cognizance Through Incremental Learning

With the tremendous increase in the use of wireless devices, understanding the surrounding wireless/RF environment is becoming essential for many application areas. In this work, we develop the technical building blocks needed for a spectrum monitoring system that can incrementally learn about the signals present in a deployed environment. We achieve incremental learning (IL) by identifying and grouping the new/unknown signals and, automatically building new machine learning (ML) models for detecting them. A thorough evaluation of our approach demonstrates its adaptability and high accuracy with signal data from several over-the-air scenarios.
Structural vulnerability of quantum networks

Structural vulnerability of a network can be studied via two key notions in graph theory: articulation points (APs) and bridges, representing nodes and edges whose removal will disconnect the network, respectively. Fundamental properties of APs and bridges in classical random networks have been studied recently. Yet, it is unknown if those properties still hold in quantum networks. Quantum networks allow for the transmission of quantum information between physically separated quantum systems. They play a very important role in quantum computing, quantum communication, and quantum sensing. Here we offer an analytical framework to study the structural vulnerability of quantum networks in terms of APs and bridges. In particular, we analytically calculate the fraction of APs and bridges for quantum networks with arbitrary degree distribution and entangled qubits in pure states. We find that quantum networks with swap operations have lower fractions of APs and bridges than their classical counterparts. Moreover, we find that quantum networks under low-degree swap operations are substantially more robust against AP attacks than their classical counterparts. These results help us better understand the structural vulnerability of quantum networks and shed light on the design of more robust quantum networks.
Semantic Textual Similarity of Sentences with Emojis

In this paper, we extend the task of semantic textual similarity to include sentences which contain emojis. Emojis are ubiquitous on social media today, but are often removed in the pre-processing stage of curating datasets for NLP tasks. In this paper, we qualitatively ascertain the amount of semantic information lost by discounting emojis, as well as show a mechanism of accounting for emojis in a semantic task. We create a sentence similarity dataset of 4000 pairs of tweets with emojis, which have been annotated for relatedness. The corpus contains tweets curated based on common topic as well as by replacement of emojis. The latter was done to analyze the difference in semantics associated with different emojis. We aim to provide an understanding of the information lost by removing emojis by providing a qualitative analysis of the dataset. We also aim to present a method of using both emojis and words for downstream NLP tasks beyond sentiment analysis.
Web Application Vulnerability Fuzzing Based On Improved Genetic Algorithm

Web fuzzing has always been an effective way to detect web vulnerabilities. Normally, traditional web fuzzing method mainly use limited test cases or generate test cases based on certain rules, which cause web fuzzing slow and inefficient. To solve this problem, we present improved genetic algorithm with a new mutation method to generate test cases. And the concept of preset functional units is proposed: test cases are divided into different functional units to ensure that the semantic structure will not be damaged during crossover and mutation. The experimental results show that the improved algorithm can generate better test cases than the standard genetic algorithm (SGA) and the adaptive genetic algorithm (AGA) and also detect more web vulnerabilities.
Machine Reading of Arabic Manuscripts using KNN and SVM Classifiers

In this paper, feature extraction techniques like Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) have been applied for feature extraction based on the structure of the Arabic Manuscript texts. Further, the two most popular classifiers namely, K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) have been used to classify these texts and the results of both the classifiers have been compared. For comparison, we have used 1155 images of Arabic words and 1100 Kufic words for the purpose of training and testing and then the results of both the classifiers have been compared. In this work, we have used the Arabic AHDB dataset and KUFIC dataset for experimentation and for selecting, training and testing the data; the partition approach has been used. It is found that SVM performs better than KNN and achieved maximum recognition accuracy of 97.05% (with KUFIC dataset) and 97.80% (with AHDB dataset).
An End-to-End Framework for Integrating and Publishing Linked Open Government Data

Linked Government Statistical Data on the Web are significantly increasing in terms of variety, which makes challenging to determine the quality of this data that explicitly become accessible to data consumers. Converting and publishing such data involves several challenges, e.g., data unifying, design decisions, knowledge extraction. In this paper, we aim to address this issue by proposing an end-to-end framework, based on linked open data technologies. This framework assists the user to produce and publish structured Linked Data in an e-Government context. Our aim is to enable data consumers to access to an end-to-end solution, allowing them to produce and publish high-quality data on the web, that meet their needs. To assess our framework, we use a real case study, relying on a local government catalogue.
A Keyword Based Technique to Evaluate Broad Question Answer Script

Evaluation is the method of assessing and determining the educational system through various techniques such as verbal or viva-voice test, subjective or objective written test. This paper presents an efficient solution to evaluate the subjective answer script electronically. In this paper, we proposed and implemented an integrated system that examines and evaluates the written answer script. This article focuses on finding the keywords from the answer script and then compares them with the keywords that have been parsed from both open and closed domain. The system also checks the grammatical and spelling errors in the answer script. Our proposed system tested with answer scripts of 100 students and gives precision score 0.91.
Improving the success rate of applying the extract method refactoring

Context: Most modern programming environments support refactorings. Although refactorings are relevant to improve the quality of software source code, they unfortunately suffer from severe usability issues. In particular, the extract method refactoring, one of the most prominent refactorings, has a failure rate of 49% when users attempt to use it. Objective: Our main objective is to improve the success rate of applying the extract method refactoring. Methods: First, to understand the cause of refactoring failure, we conducted a partial replication of Vakilian's ICSE '14 study about usability issues of refactoring using IntelliJ IDEA. Second, we designed and implemented TOAD, a tool that proposes alternative text selection for source code refactoring for the Pharo programming language. Third, we evaluated TOAD using a controlled experiment against the standard Pharo code refactoring tool. Seven professional software engineers complemented with three undergrad students participated in our experiments. Conclusion: The causes we identified of failed extract method refactoring attempts match Vakilian's work. TOAD significantly reduces the number of failed attempts to run the extract method refactoring at a lower cognitive load cost.
A Novel Model Of Smart Education For The Development Of Smart University System

The advancement and proliferation of smart and wireless technologies are transforming the educational process into a smart system that provides seamless learning process. Smart education has been found to support the needs and learning style of students, and offer opportunities for adaptive, personalised, collaborative and self-learning processes. However, despite its' increasing use to support the learning process, there remained limited understanding of the components of the smart education system and how these components interact to contribute to the development of smart education system. This paper explored the literature to understand these limitations and provide a broader and comprehensive definition that captured the components that support the development of smart education. Based on this view, four component-based model of smart education was proposed. These components include smart pedagogy, smart learning environment, smart technologies and smart learners with each contributing to the development of smart education system initiatives. This model can provide a basis for the development of a research process in a smart education for improved learning experiences.
Deep Loss Driven Multi-Scale Hashing Based on Pyramid Connected Network

Thanks to the great success of the deep learning, deep hashing for large-scale multimedia retrieval has made significant progress recently. However, most existing deep hashing algorithms suffer from slow convergence due to the gradient vanishing problem, caused by deep network structures and saturated activation functions. Moreover, a single convolution layer is often followed by down-sampling such as max pooling, resulting in local information loss that might affect the overall system robustness and performance. In this work, we propose a novel deep supervised hashing, Deep Loss Driven Multi-Scale Hashing (DLDMSH), which learns the high-quality approximate binary codes through an end-to-end network and improves the representative capacity of hash codes for large-scale image retrieval. Specifically, we design a Loss Driven Multi-Scale (LDMS) feature which is aggregated from convolutional feature maps. Moreover, a Pyramid Connected Convolutional Neural Network (PCNet) architecture is devised to generate LDMS feature, which inputs pairs of images during the training and outputs an image to approximate discrete values. In particular, 1 × 1 convolution kernels are applied to make a linear combination of features for realizing feature reduction, and the reduced features are fused in the fusion layer. This effectively improves the performance of deep features. A novel loss function preserving semantic information is integrated into an end-to-end learning scheme, which enhances the representative capacity of binary codes. Extensive experiments over four benchmark datasets show that DLDMSH significantly outperforms several other state-of-the-art hashing methods.
Investigating the discrepancy property of de Bruijn sequences

The discrepancy of a binary string refers to the maximum (absolute) difference between the number of ones and the number of zeroes over all possible substrings of the given binary string. We provide an investigation of the discrepancy of over a dozen simple constructions of de Bruijn sequences as well as de Bruijn sequences based on linear feedback shift registers whose feedback polynomials are primitive. Furthermore, we demonstrate constructions that attain the lower bound of Θ(n) and a new construction that attains the previously known upper bound of Θ(2nn). This extends the work of Cooper and Heitsch [Discrete Mathematics, 310 (2010)].
AIBangla: A Benchmark Dataset for Isolated Bangla Handwritten Basic and Compound Character Recognition

Automatic handwritten Bangla character recognition (HBCR) is a challenging problem in computer vision due to numerous variations in writing styles of an individual Bangla character and the presence of similarities in shapes among different characters. Considering the complexity of the problem, we need to develop a modern convolutional neural network (CNN) for accurate recognition, but unfortunately, at present, very few Bangla handwritten dataset contain a large number of image samples for each character suitable for training deep learning-based methods. In this paper, we present AIBangla, a new benchmark image database for isolated handwritten Bangla characters with detailed usage and a performance baseline. Our dataset contains 80,403 hand-written images on 50 Bangla basic characters and 249,911 hand-written images on 171 Bangla compound characters which were written by more than 2,000 unique writers from various institutes across Bangladesh. In addition, we have applied three leading state-of-the-art deep CNN networks on our proposed AIBangla dataset to provide baseline performance. We have achieved a maximum accuracy of 98.13% and 81.83% for basic and compound character classes respectively on the test set of the AIBangla dataset.
Study of Cloud Security in Hyper-scalers

“Hyper-scalers” are the next big thing that are ready to radically change the online world for data storage though the means of various services provided by them in all spheres of technology. By hyper-scalers we mean big tech giants like Google, Amazon, Alibaba Cloud etc. who are happily venturing and rising in the world of cloud services. They key aspect involved in the major shift from on-premise to cloud world is 1) On-fly scaling of storage/service requirements and 2) The various layers of security provided by these cloud giants. This research is aimed at studying the current trends observed in the field of cloud computing with regards to cloud acceptance, growth and security trends prevailing in the ongoing decade; comparing the principles of cloud security being implemented by different hyper-scalers and also to try and analyze the trends of cloud security issues in past years. Overview of some major suggestions on how cloud security can be improved have also been discussed.
Formal verification of braking while swerving in automobiles

Many vehicle accidents result from collision with foreign objects. Automatic and provably safe collision avoidance systems are thus of prime importance to the automobile industry. Previous work on formally verifying car collision avoidance maneuvers typically only focuses on braking-only or swerving-only maneuvers. In this work, we study combined braking and swerving maneuvers and establish formally verified conditions under which safety from collision is ensured. One major constrain in performing such joint maneuvers is that a vehicle's tires have limited traction which can be used either for braking or swerving. So in essence, a combined maneuver can trade off braking ability for turning when it is advantageous to do so and vice-versa. In this work, we study the full continuous range of combined maneuvers, from maximal turning with little braking to maximal braking with little turning.
A Better Set of Object-Oriented Design Metrics for Within-Project Defect Prediction

Background: Using design metrics to predict fault-prone elements of a software design can help to focus attention on classes that need redesign and more extensive testing. However, some design metrics have been pointed out to be theoretically invalid, and the usefulness of some metrics is questioned.
Urdu Spell Checker: A Scarce Resource Language

In the digital world of computers, several software applications have been developed to ensure spellings of various words. English language is found to have gone far ahead in the development of spell checking applications whilst other languages specifically naming Urdu, lack behind to cherish such technologies. We develop “Urdu Spell Checker” which detects incorrect spellings of a word and provides a list of options containing correct spellings. The spell checker carries correct spellings of words residing inside a predefined lexicon or corpus. It is to ensure whether entered word is correct or not. In case if the input word matches with the corpus words it is considered correct otherwise it is considered as misspelled word. Multiple techniques are used individually as well as a combination these techniques is used to check which set of methods is best in terms of output. By using multiple techniques for error correction, it is observed that Jaro distance provides best results with combination of soundex, shapex and n-gram that is 80.0% precision, 44.87% recall and 57.37% F-Measure.
PansyTree: Merging Multiple Hierarchies

Hierarchical structures are very common in the real world for recording all kinds of relational data generated in our daily life and business procedures. A very popular visualization method for displaying such data structures is called "Tree". So far, there are a variety of Tree visualization methods that have been proposed and most of them can only visualize one hierarchical dataset at a time. Hence, it raises the difficulty of comparison between two or more hierarchical datasets.In this paper, we proposed Pansy Tree which used a tree metaphor to visualize merged hierarchies. We design a unique icon named pansy to represent each merged node in the structure. Each Pansy is encoded by three colors mapping data items from three different datasets in the same hierarchical position (or tree node). The petals and sepal on Pansy are designed for showing each attribute’s values and hierarchical information. We also redefine the links in force layout encoded by width and animation to better convey hierarchical information. We further apply Pansy Tree into CNCEE datasets and demonstrate two use cases to verify its effectiveness.The main contribution of this work is to merge three datasets into one tree that makes it much easier to explore and compare the structures, data items and data attributes with visual tools.
Volumetric interpolation of tomographic sequences for accurate 3D reconstruction of anatomical parts

Background and objective:Tomographic sequences of biomedical images are commonly used to achieve a three-dimensional visualization of the human anatomy. In some cases, the number of images contained in the sequence is limited, e.g., in low-dose computed tomography acquired on neonatal patients, resulting in a coarse and inaccurate 3D reconstruction. Methods:In this paper, volumetric image interpolation methods, devised to increase the axial resolution of tomographic sequences and achieve a refined 3D reconstruction, are proposed and compared. The techniques taken into consideration are based on motion-compensated frame-interpolation concepts, which have been developed for video applications, mainly frame-rate conversion. Results: The performance of the proposed methods is quantitatively assessed by using sequences with a simulated low axial resolution obtained from the decimation of standard high-resolution computed tomography sequences. Real data with an actual low axial resolution have been used as well for a qualitative evaluation of the proposed methods. Conclusions:The experimental results demonstrate that the proposed methods enable an effective slice interpolation and that the achievable 3D models clearly benefit from the increased axial resolution.
Requirements Traceability Management Support Tool for UML Models

Traceability between system requirements and the artifacts produced by the software development process contributes to the development of reliable software. This paper proposes a method of ensuring traceability by automatically generating an initial sequence diagram from an activity diagram; the activity diagram not only defines user-system interactions, but it also describes the feasibility of use cases via data modeling in a class diagram. These sequence diagrams evolve through changes that are made to the particulars of a system design; these sequence diagrams can be used to generate source code. As a result, the requirements can be traced to the program codes.
A Deep Learning Framework for Automated Transfer Learning of Neural Networks

Transfer Learning is a technique that reduces the time taken for training and improves performance by reusing the weights of a previously trained source neural network. This poses a question of how the source network must be chosen, which is still an unsolved problem. In this work, we have built a framework that automatically performs transfer learning by selecting the source neural network based on an estimate of dataset classification difficulty. The framework designates the neural network of the dataset that is closest in difficulty as the source. The framework is evaluated on 7 datasets namely SVHN, Cifar10, Cifar100, GTSRB, MNIST, Flowers and Linnaeus5, and experimental results suggest that in most cases this type of source selection gives the highest improvement in accuracy. The framework provides an average improvement in accuracy of 6.7% for ResNet than when training from scratch, and achieves it within the first 10 epochs in some cases.