Reviewerly/exHarmony huggingface dataset:
Design of a Transliteration Application for Kannada Signboards

A common problem faced by any non-native people and travelers from other states visitingKarnataka is the inability to read the Kannada text in public places like street boards, bus names and signboards. So, the transliteration from Kannada to English is essential in such cases. Transliteration is the process which will convert the script of one language to other language without altering the meaning. This paper focuses on different approaches to solve the problems of reading Kannada text using machine learning techniques. We have used Tesseract android software development kit(SDK) for Optical character Recognition. LIBINDIC Soundex Algorithm is used for the transliteration purpose. Main aim of the paper is to build an app to capture the image, process it and also to have its transliterated output.
Robust Redirected Walking in the Wild

Locomotion is a fundamental component of experiences in virtual reality (VR). However, locomotion in VR is often difficult because the layouts of the physical and virtual environments are often different, which may cause unobstructed paths in the virtual world to correspond to obstructed paths in the physical world. Thus, in order to deliver a comfortable and immersive virtual experience to users, it is important that the user can explore the virtual world using techniques that help them avoid collisions with unseen physical objects. Redirected walking (RDW) is one such technique that enables collision-free locomotion in VR using real walking. Although RDW shows promise as an effective locomotion interface, it has seen relatively little adoption in the consumer market due to the difficulty in deploying effective RDW algorithms that are robust to different environment layouts and different users&#x2019; perceptual thresholds. For my thesis, I am focused on developing RDW methods that are capable of enabling collision-free locomotion in arbitrary physical and virtual environments for a wide range of users.
Optimization in Object Detection Model using YOLO.v3

Object Detection is one of the important entities in the field of Computer Vision with a large number of applications. This project demonstrates Object detection using You Only Look Once (YOLO) Algorithm, version 3. YOLOv3 method is prominently used in object detection methods which are based on Deep Learning. It uses k-means cluster method for creating bounding boxes of specific height and width, which are used for predicting output. The model training is based on the Common Object in Context (COCO) Dataset. The dataset has around 164K images based on 80 categories, also called as classes. Thus, this object detection model takes an image from the user and then with the help of YOLO algorithm, predicts the types of objects present in that image and marks them accurately., the lower complex CNN model achieves an accuracy of 0.93.
Artificial colorization of digitized microfilms: a preliminary study

A lot of available digitized manuscripts online are actually digitized microfilms, a technology dating back from the 1930s. With the progress of artificial colorization, we make the hypothesis that microfilms could be colored with these recent technologies, testing InstColorization. We train a model over an ad-hoc dataset of 18 788 color images that are artificially gray-scaled for this purpose. With promising results in terms of colorization but clear limitations due to the difference between artificially grayscaled images and "naturaly" greyscaled microfilms, we evaluate the impact of this artificial colorization on two downstream tasks using Kraken: layout analysis and text recognition. Unfortunately, the results show little to no improvements which limits the interest of artificial colorization on manuscripts in the computer vision domain.
Guided Semi-Supervised Non-Negative Matrix Factorization

Classification and topic modeling are popular techniques in machine learning that extract information from large-scale datasets. By incorporating a priori information such as labels or important features, methods have been developed to perform classification and topic modeling tasks; however, most methods that can perform both do not allow for guidance of the topics or features. In this paper, we propose a novel method, namely Guided Semi-Supervised Non-negative Matrix Factorization (GSSNMF), that performs both classification and topic modeling by incorporating supervision from both pre-assigned document class labels and user-designed seed words. We test the performance of this method on legal documents provided by the California Innocence Project and the 20 Newsgroups dataset. Our results show that the proposed method improves both classification accuracy and topic coherence in comparison to past methods such as Semi-Supervised Non-negative Matrix Factorization (SSNMF), Guided Non-negative Matrix Factorization (Guided NMF), and Topic Supervised NMF.
Touch the History in Virtuality: Combine Passive Haptic with 360° Videos in History Learning

History learning can help learners understand changes and establish identity. 360&#x00B0; videos can display complex history scenes, but limited to the visual and auditory. Passive haptic can provide tactile feedback by matching physical props to virtual counterparts. Utilizing passive haptic in 360&#x00B0; videos can create a diverse and rich experience but face matching problems because of perspective distortion. We propose using high-fidelity models and mapping videos to a cube space to solve this problem. The results of a 28 participants user study show that combining passive haptic with 360&#x00B0; videos enhances users&#x0027; presence in historical experience.
Digital Transformation as a Reconstruction of Knowledge

The subject of this paper is a description of the renew and more advanced idea of digital transformation, which is understood not as a contemporary process of the impact of digital technologies on social life, but as an epistemological change in geometry and mathematics in the 19th century. Turing machine was a result of this change as well as the new idea of axiomatic systems. The first brought back the concept of computability as an essence of the world, and the second was a new and arbitrary model of reasoning. Both changed the image of the social world.
Face Recognition based on Multi-shape Morphological Profiles-based Covariance Descriptors and Log- Etuclidean Kernel SVM

Face recognition (FR) is one of the important areas of image processing that allows us to recognize individuals from gathered face images. It has been shown in the literature that contextual information of the face can improve the performance of FR algorithms. This paper proposes an efficient face recognition method based on multi-shape morphological profiles (MMPs), covariance descriptors, and log-Euclidean kernel SVM. In the first stage of this method, MMPs containing contextual information are generated from the original faces. The covariance descriptors of the MMPs are produced in the subsequent stage. Finally, these covariance descriptors are classified using log-Euclidean kernel SVM. To compare the findings, we ran our tests on ORL face dataset. The mean accuracy of the proposed method is 96.4 on the ORL face datasets, respectively, demonstrating that the proposed method outperforms some of the existing state-of-the-art FR methods.
[DC] Mixed Reality Interaction for Mobile Knowledge Work

Knowledge workers typically work on some kind of computer and other than an internet connection, they rarely need access to specific work environments or devices. This makes it feasible for them to also work in different environments or in mobile settings, like public transportation. In such spaces comfort and productivity could be decreased due to hardware limitations like small screen sizes or input devices and environmental clutter. Mixed reality (MR) has the potential to solve such issues. It can provide the user with additional display space that can even include the third dimension. It can open up new possibilities for interacting with virtual content using gestures or spatially tracked devices. And it can allow the users to modify the work environment according to their personal preferences. This doctoral thesis aims at exploring the challenges of using MR for mobile knowledge work and how to effectively support knowledge worker tasks through appropriate interaction techniques.
EyeMove - Towards Mobile Authentication using EOG Glasses

Existing approaches for mobile authentication are prone to shoulder-surfing and side-channel attacks. Using gazes for authentication has been demonstrated to be more resistant to these attacks. Yet, existing solutions rely on eye-tracking by the device’s front camera that is not always reliable. In this paper, we present an approach for EOG-based authentication by determining the gaze-based on the electronic potential of the eyes. Our approach runs on commercially available smart glasses and there is no need for the user to look at the device. Through a user study with 15 participants, we demonstrate the feasibility of the approach, its usability, and its adoption by mobile device users. Even users without regular glasses would adopt EOG-based authentication.
An Under-sampling Algorithm Based on Weighted Complexity and Its Application in Software Defect Prediction

The under-sampling technique is an important method to solve the class imbalance issue in software defect prediction. However, the existing under-sampling methods generally ignore the problem that there are great differences in the complexities of different samples. In fact, the complexities of samples can play an important role in defect prediction, since there is a close relation between the complexities of samples and whether they have defects. Therefore, when we use the under-sampling technique to handle the class imbalance issue in software defect prediction, it is necessary to consider the complexities of samples. In this paper, we propose the notion of weighted complexity. When calculating the weighted complexity of each sample, the weights of different condition attributes are considered. Based on the weighted complexity, we propose a new under-sampling algorithm, called WCP-UnderSampler, and apply it to software defect prediction. In WCP-UnderSampler, we first employ the granularity decision entropy in rough sets to calculate the significance and the weight of each condition attribute; Second, the weighted complexity of each sample is obtained by calculating the weighted sum of the values of the sample on all attributes; Third, the majority class samples are sorted in descending order according to their weighted complexities, and the majority class samples with higher complexities are selected until a balanced data set is obtained. Experiments on defect prediction data sets show that we can obtain better software defect prediction results by using WCP-UnderSampler to handle the imbalanced data.
Implementation of Multiple Feature Selection Algorithms for Speech Spoofing Detection

Abstract The ASVspoof challenge sequences were proposed to lead the research in anti-spoofing to a new level for automatic speaker verification (ASV). It’s verified that constant Q cepstral coefficients (CQCC) processes speech in variable frequencies with adjustable resolution and outperforms the other generally used features and Linear Frequency Cepstral Coefficient (LFCC) is used in high-frequency areas. The feature selection algorithm is offered to decrease computational complexity and overfitting for spoofed utterance detection. Precisely, there’s a demand for feature selection algorithms that are computationally effective and sensitive to feature interactions so that useful features aren’t falsely excluded during the ranking process. We experiment on the ASVspoof 2019 challenge for the assessment of spoofing countermeasures. After the evaluation of our given algorithms and data gives us an equal error rate (EER) and tandem discovery cost function (t-DCF) values. Experimental results on ASVspoof 2019 physical access referring to multiple feature selection approaches show a breakthrough compared to the baseline.
Research on Automatic Cargo Recognition in Smart City Environment

Smart city refers to the use of various information technologies to improve the lives of citizens. However, in terms of transportation and sales of goods, traditional methods require a lot of manpower and material resources, and cannot be automatically identified. In order to improve the efficiency and accuracy of product identification, product sorting is automated. It uses the powerful feature learning and expression capabilities of deep convolutional neural networks to automatically learn product features, thereby achieving high-precision image classification. Therefore, this paper first proposes an improved VGG network, combines transfer learning to establish a deep learning recognition model, and finally conducts multiple sets of experiments on the 131-category Fruit-360 dataset. The results show that when the Adam optimizer is used for iterative training for 30 rounds and the batch_size is 64, the accuracy of the algorithm proposed in this paper reaches 94.19% on the training set, 97.91% on the validation set, and 92.2% on the test set top1. The accuracy rate on the test set top5 is as high as 100%. Therefore, the method in this paper can solve the problems caused by traditional methods and provide useful help for smart cities.
Breast Cancer Segmentation in Mammogram Using Artificial Intelligence and Image Processing: A Systematic Review

Background: Breast cancer is the second leading cause of death in females worldwide. Mammograms are useful in early cancer diagnosis as well when the patient can sense symptoms or they become observable. Inspection of mammograms in search of breast tumors is a difficult task that radiologists must carry out frequently. Objective: This paper provides a summary of possible strategies used in automated systems for a mammogram, especially focusing on segmentation techniques used for cancer localization in mammograms. Methods: This article is intended to present a brief overview for nonexperts and beginners in this field. It starts with an overview of the mammograms, public and private available datasets, image processing techniques used for a mammogram and cancer classification followed by cancer segmentation using the machine and deep learning techniques Conclusion: The approaches used in these stages are summarized, and their advantages and disadvantages with possible future research directions are discussed. In the future, we will train a model of medical images that can be used for transfer learning in mammograms.
Unlimited dynamic range signal recovery for folded graph signals

Recovery of a graph signal from samples has many important applications in signal processing over networks and graph-structured data. To capture very high or even unlimited dynamic range signals, modulo sampling has been investigated. Folded signals are signals generated from a modulo operation. In this paper, we investigate the problem of recovering bandlimited graph signals from folded signal samples. We derive sufficient conditions to achieve successful recovery of the graph signal, which can be achieved via integer programming. To resolve the scalability issue of integer programming, we propose a sparse optimization recovery method for graph signals satisfying certain technical conditions. Such an approach requires a novel graph sampling scheme that selects vertices with small signal variation. The proposed algorithm exploits the inherent relationship among the graph vertices in both the vertex and time domains to recover the graph signal from folded samples. Simulations and experiments validate the feasibility of our proposed approach.
Geometric Calibration with Multi-Viewpoints for Multi-Projector Systems on Arbitrary Shapes Using Homography and Pixel Maps

We propose a geometrical calibration method for the MISATO astronomical observatory in Japan. The primary objective of our projection system is enabling a large-scale geometrical projection calibration for near-planner arbitrarily-shaped ground projection with a multi-projector system via temporally-placed camera capturing. The obtained results exhibited an average distortion reduction of 84.7&#x0025; less than the simple homography-based method.
Adapting the Scrum Framework to the Needs of Virtual Teams of Game Developers with Multi-site Members

ABSTRACT. As we all know, most video games are developed using the well-known Scrum framework as the core approach for managing the project. Indeed, Scrum is an effective value-driven approach allowing adjustments based on regular and repeated feedback. Nowadays, in the video games industry, it is frequent to see virtual teams, with fragmented groups of people, working remotely from multiple sites. However, the Scrum framework is not designed to fits the needs of a fragmented multi-site team. Scrum was designed for small teams where all members are supposed to work together on the same location, having frequent face-to-face contacts in, ideally, the same open space. When applied on a virtual team, the Scrum framework is less effective because it does not offer sufficient tools and artefacts for addressing the specific problems emerging from the virtual context, such as the communication problem, the isolation of people of the groups, the challenge of keeping transparency, etc. We propose, in this paper, a comprehensive extension of the Scrum framework allowing to alleviate some of these issues. We also present the results of a first experiment with two virtual teams developing video games.
An Improved Wild Horse Optimizer for Traveling Salesman Problem

Traveling salesman problem (TSP) is well-known combinatorial optimization problems. Due to its importance in many applications such as engineering sciences, path planning, and sensor placement, many researchers have been attracted to solve this problem. In this paper, a new improved version of Wild horse optimizer (I-WHO) is proposed to boost its performance in solving global optimization and combinatorial optimization problems. To examine the performance of I-WHO, the obtained results are compared with state-of-the-art algorithms. To have an unbiased and accurate comparison, descriptive statistics such as standard deviation, mean, and Wilcoxon rank-sum test are also used. The computational result showed that I-WHO significantly outperforms other alternative algorithms.
Secure Authentication Scheme for Medical care applications based on IoT

In the medical business, the Internet of Medical Things (IoMT) has turned into a specialist application framework. It is utilized to accumulate and break down the physiological boundaries of patients. The vital body parameters are analyzed by the medical sensor-hubs, which are embedded in the patient&#x0027;s body. It would thusly detect the patient&#x0027;s medical data by utilizing convenient gadgets. Since patient data is so delicate to uncover without the help of a medical expert, the security and protection of medical information are turning into a difficult issue for the IoMT. Hence, an anonymous authentication protocol based on IoT is likely to be presented in this paper. This work is liked to determine the security protection issues in the IoMT. In this work, a secure and anonymous patient and medical advisor authentication scheme is proposed to guarantee secure correspondence in medical care applications. The validation of the work is evaluated in terms of computational cost with different existing schemes using the Cygwin platform.
CLAD: A Deep Learning Framework for Continually Learning in Anomaly Detection

The rapid development and frequent revolutions in information technology (like Edge Computing, Wireless Sensor Network) highlight the significance of Internet of Things. Nowadays, a variety of infrastructures from diverse fields are limited by external and internal environmental factors. In actual operation, these factors may cause serious anomalies and aggravate the burden of facilities’ maintenance. With a rise in the number and running time, the performance of these facilities would be unstable and complex. This paper proposes a deep learning framework (CLAD) to do adaptive anomaly detection. It implements dynamic anomaly thresholding based on the prediction of incremental Long Short-Term Memory. This framework employs a replay buffer to solve the decline of detection accuracy. With this framework, models can keep perfect detection accuracy under a quite high load (time and number). This framework is valuable for further research of adaptive anomaly detection.
Arthur N. Prior and the Lvov-Warsaw School

This paper presents the link between Arthur N. Prior and logicians that belonged to the Lvov-Warsaw School. Although certain members of the Lvov-Warsaw School influenced Prior’s views, the amount and the form of the impact are still under discussion. Prior also cooperated with some of them in the development of his systems of logic. This paper focuses on four main areas in which Prior admitted adopting ideas from the Lvov-Warsaw School: systems of propositional logic, the history of logic, modal and temporal logic and ontology. Prior’s published works as well as his correspondence, which is stored in Prior’s Nachlass, are used to describe the influence.
A Task-Parallel and Reconfigurable FPGA-Based Hardware Implementation of Extreme Learning Machine

Extreme learning machine (ELM) is an emerging machine learning algorithm and widely used in various real-world applications due to its extremely fast training speed, good generalization and universal approximation capability. In order to further explore the ELM to be used in practical embedded systems, a task-parallel and reconfigurable FPGA-based hardware architecture of ELM algorithm is presented in this paper. The proposed architecture performs the on-chip machine learning for both training and prediction phases which are implemented parameterizably based on the reconfigurable parameters. Meanwhile, the task-parallel efforts are focused on the training phase to improve the computational efficiency by resolving the serial computations into subtasks for task-parallel computations. In addition, the on-chip block RAMs reuse scheme is also applied in proposed architecture for saving on-chip resource consumption. The experimental results show that the proposed ELM architecture can achieve similar accuracy compared with floating-point implementation on Matlab and outperform the recently published ELM implementations in terms of hardware performance, power consumption and resource utilization.