Reviewerly/exHarmony huggingface dataset:
A Novel Approach for Optical Character Recognition (OCR) of Handwritten Telugu Alphabets using Convolutional Neural Networks

Telugu is a South Indian language that more than 10 Crore people spoke in this world. The alphabets of the Telugu language are more complex, alpha-syllabary, and agglutinative in nature. Different alphabets are like each other and will overlap with other alphabets. Due to its core, the handwritten alphabets in the images cannot be detected easily by the existing optical character recognition system. Our project will create a dataset of various representations of Telugu alphabets to avoid these problems. The proposed system can be trained to detect the character present in the images. This paper proposes a new technique to identify the handwritten alphabets of Telugu language in the pictures by using a Deep Convolutional Neural Network. A remarkable improvement has been achieved by recognizing the individual alphabets with an overall accuracy of 80% - 95% with the proposed model.
Wordhyve: A context-aware language learning app for vocabulary enhancement through images and learning contexts

Vocabulary acquisition is an essential component for mastering any language as words are the building blocks of a language. In informal learning, foreign language learners often struggle to memorize new vocabularies, and therefore, new tools need to be developed to facilitate vocabulary acquisition. In computer-assisted learning environments, images are often used as annotations to represent words because images convey the essence of a word more effectively than verbal descriptions. Also, understanding the learning contexts in which learning happens is crucial for any computer-assisted learning environment. From this standpoint, in this research, a context-aware language learning app called Wordhyve is developed. Wordhyve, a native Android app, is built to support foreign language learners in memorizing foreign vocabularies using multimedia annotations, including images, texts, translation, voices, and contextual clues. Wordhyve allows language learners to capture and record lifelogs. Later on, the app uses those learning experiences as triggers to enhance foreign vocabularies. The analytics of the Wordhyve use those logs for the recommendation of incidental vocabularies and assist learners in memorizing those recommended vocabularies using various learning contexts. Wordhyve uses image analytics on the learner-captured images to recommend those incidental vocabularies.
An optimized framework for digital watermarking based on multi-parameterized 2D-FrFT using PSO

In this paper, we propose a multi-parameter image watermarking algorithm using the fractional Fourier transform (FrFT). The proposed algorithm employs a double transform mechanism to increase the robustness of the algorithm against attacks. We also couple the application of the transform with the Particle Swarm Optimization (PSO) algorithm to decide on the ideal fractional domain for the embedding process. The PSO finds the optimal fractional domain by minimizing the RMSE between the input image and the watermarked image. The results obtained are tested against standard parameters, e.g., robustness, imperceptibility, payload capacity, to evaluate the performance of the algorithm.
Deepfake Video Authentication Based on Blockchain

Nowadays, it is difficult to predict the information such as news and videos on the internet is real or not and people are increasingly sharing it on social media without thinking of fake. As soon as they verify the authenticity of a video, they start expressing their concerns and sharing the opinions of others. Therefore, such videos spread rapidly and help in sharing fake videos or unverified information. The need today is to focus on building a strong fake video detection system as soon as possible to avoid the consequences of such unverified information. This paper analyzes the recent research articles to detect the deep fake videos on social media.
Mobile Robot Obstacle Avoidance Based on Improved Artificial Potential Field Method

Aiming at the defects of the traditional artificial potential field method in the obstacle avoidance process of mobile robots, the problems of unreachable targets and local minimum are analyzed. Introduce the distance index between the robot and the obstacle into the gravitational field function to solve the problem of unreachable target. Get rid of local minimum by applying escape force. Aiming at the problem that the traditional artificial potential field method is not effective in the dynamic environment, it is improved by adding a speed factor to the repulsive force field function. Finally, the improved artificial potential field method is applied in the complex dynamic environment. Using MATLAB to compare and simulate the improved algorithm, the experimental results verify the effectiveness and feasibility of the method.
Prototyping Smart Eyewear with Capacitive Sensing for Facial and Head Gesture Detection

The prevalence of smart eyewear, particularly in the form of eye glasses, has continuously increased. In this paper, we explore instrumenting a pair of glasses with Capacitive Sensing (CapSense) technology. We conducted three studies, in which we (1) explore a suitable CapSense setup for rapid prototyping; (2) reveal the potential of detecting facial and head gestures with a CapSense glasses prototype; and (3) investigate the technical feasibility of using a passive electric field sensing. Instead of training a low-dimensional gesture set that works across different users (e.g., eye winks), we selected a set of 12 facial and head related gestures, which we classified by relying on user-dependent machine learning models.
Towards efforts to enhance tax revenue mobilisation in Africa: Exploring the interaction between industrialisation and digital infrastructure

Motivated by the momentous rise in the new economy, the implementation of the African Continental Free Trade Area agreement, and the expected rebound of foreign direct investment to Africa from 2022, this study examines the joint effects of industrialisation and digital infrastructure on resource mobilisation in Africa. To this end, we mine data for 42 African countries for the analysis. The results, which are based on the system GMM estimator show that although unconditionally both industrialisation and digital infrastructure enhance (i) goods and services tax (GST), and (ii) profits, corporate and income tax (PCIT) mobilisation efforts in Africa, the effects of the former is rather remarkable in the presence of the latter. Particularly, we find that although all our digital infrastructure dynamics amplify the effect of industrialisation on GST, only ICT usage and ICT skills matter for PCIT. Second, the study unveils ICT thresholds for complementary policies. Accordingly, industrialisation and ICTs are necessary and sufficient conditions for tax revenue mobilisation only below some ICT thresholds. Above these ICT thresholds, complementary policies are needed to maintain the overall positive incidence on tax revenue mobilisation. Policy recommendations are provided in the end.
Just a Few Points are All You Need for Multi-view Stereo: A Novel Semi-supervised Learning Method for Multi-view Stereo

While learning-based multi-view stereo (MVS) methods have recently shown successful performances in quality and efficiency, limited MVS data hampers generalization to unseen environments. A simple solution is to generate various large-scale MVS datasets, but generating dense ground truth for 3D structure requires a huge amount of time and resources. On the other hand, if the reliance on dense ground truth is relaxed, MVS systems will generalize more smoothly to new environments. To this end, we first introduce a novel semi-supervised multi-view stereo framework called a Sparse Ground truth-based MVS Network (SGT-MVSNet) that can reliably reconstruct the 3D structures even with a few ground truth 3D points. Our strategy is to divide the accurate and erroneous regions and individually conquer them based on our observation that a probability map can separate these regions. We propose a self-supervision loss called the 3D Point Consistency Loss to enhance the 3D reconstruction performance, which forces the 3D points back-projected from the corresponding pixels by the predicted depth values to meet at the same 3D co-ordinates. Finally, we propagate these improved depth pre-dictions toward edges and occlusions by the Coarse-to-fine Reliable Depth Propagation module. We generate the spare ground truth of the DTU dataset for evaluation and extensive experiments verify that our SGT-MVSNet outperforms the state-of-the-art MVS methods on the sparse ground truth setting. Moreover, our method shows comparable reconstruction results to the supervised MVS methods though we only used tens and hundreds of ground truth 3D points.
Weakly Supervised Compositional Metric Learning for Face Verification

The aim of metric learning is to learn a mapping relationship which reduces the intra-class distance and increase the inter-class distance. As there are a large number of parameters need to be optimized in traditional metric learning algorithms, they may suffer from high computational complexity and overfitting problems in the case of insufficient training data. To alleviate this, we propose a weakly-supervised compositional metric learning (WSCML) method, which utilizes a set of the predetermined local discriminant metrics to learn the optimal weight combination of the component metrics. Under the large margin framework, our WSCML can effectively improve the verification accuracy by constraining the Mahalanobis distance of positive sample pairs to be less than a small threshold and that of negative sample pairs to be greater than a large threshold. In addition, we employ three regularization terms to optimize the proposed WSCML respectively to control the sparsity of the solution while maintaining its feasibility. Experimental results on KinFaceW-I, FGFV and LFW datasets show the effectiveness of the proposed method.
Risk Factors for Severe COVID-19 in a Large Medical Records Linkage System in the United States

StatesM ore than a year after the first cluster of patients was reported, coronavirus disease 2019 (COVID-19) remains a serious public health threat.During the pandemic, epidemiologic studies have been playing an important role in the response to COVID-19 by providing health care professionals and policymakers with necessary data.Identification of risk factors is one of the fundamental cores of epidemiologic studies because such knowledge is essential to understand disease characteristics, to prioritize resources for vulnerable populations, and to campaign for raising the awareness.In this issue of Mayo Clinic Proceedings, St. Sauver and colleagues add quality evidence to the epidemiology of COVID-19. 1 The authors explored risk factors associated with severe COVID-19 among patients who tested positive for COVID-19 using data from the Rochester Epidemiology Project, an integrated medical records linkage system that covers approximately 1.7 million individuals (or 61% of the population) in southern Minnesota and western Wisconsin in the United States.
Towards Neural Schema Alignment for OpenStreetMap and Knowledge Graphs

OpenStreetMap (OSM) is one of the richest, openly available sources of volunteered geographic information. Although OSM includes various geographical entities, their descriptions are highly heterogeneous, incomplete, and do not follow any well-defined ontology. Knowledge graphs can potentially provide valuable semantic information to enrich OSM entities. However, interlinking OSM entities with knowledge graphs is inherently difficult due to the large, heterogeneous, ambiguous, and flat OSM schema and the annotation sparsity. This paper tackles the alignment of OSM tags with the corresponding knowledge graph classes holistically by jointly considering the schema and instance layers. We propose a novel neural architecture that capitalizes upon a shared latent space for tag-to-class alignment created using linked entities in OSM and knowledge graphs. Our experiments aligning OSM datasets for several countries with two of the most prominent openly available knowledge graphs, namely, Wikidata and DBpedia, demonstrate that the proposed approach outperforms the state-of-the-art schema alignment baselines by up to 37% points F1-score. The resulting alignment facilitates new semantic annotations for over 10 million OSM entities worldwide, which is over a 400% increase compared to the existing annotations.
Evaluating fallback authentication research: A systematic literature review

Fallback authentication refers to a chain of alternative authentication schemes that are issued if the main authentication method fails such as the provision of information on credentials and security challenge questions. The user authentication domain includes a number of contribution efforts to increase the usability and security of fallback authentication systems. The aim of this study is to collect the existing knowledge on fallback authentication techniques and related issues through a systematic literature review. In addition, this paper explores the frameworks that can be used to compare fallback authentication techniques according to different criteria. A total of 70 articles were reviewed, different fallback authentication techniques have been found and 9 frameworks were discussed. The lack of frameworks regarding the comparison of fallback authentication schemes is observed. Finally, this study discusses the research gaps and recommendations that are critical to fallback authentication.
A Secure Key Aggregate Searchable Encryption with Multi Delegation in Cloud Data Sharing Service

As the amount of data generated in various distributed environments is rapidly increasing, cloud servers and computing technologies are attracting considerable attention. However, the cloud server has privacy issues, including personal information and requires the help of a Trusted Third Party (TTP) for data sharing. However, because the amount of data generated and value increases, the data owner who produces data must become the subject of data sharing. In this study, we use key aggregate searchable encryption (KASE) technology, which enables keyword search, to efficiently share data without using TTP. The traditional KASE scheme approach only discusses delegation of authority from the data owner to another user. However, if the delegated entity cannot perform time-critical tasks because the shared data are unavailable, the delegate must further delegate the rights given to other users. Consequently, this paper proposes a new KASE scheme that enables multi-delegation without TTP and includes an authentication technique between the user and the server. After that, we perform informal and formal analysis using BAN logic and AVISPA for security evaluation, and compare the security and performance aspects with existing schemes.
Interaction Compass: Multi-Label Zero-Shot Learning of Human-Object Interactions via Spatial Relations

We study the problem of multi-label zero-shot recognition in which labels are in the form of human-object interactions (combinations of actions on objects), each image may contain multiple interactions and some interactions do not have training images. We propose a novel compositional learning framework that decouples interaction labels into separate action and object scores that incorporate the spatial compatibility between the two components. We combine these scores to efficiently recognize seen and unseen interactions. However, learning action-object spatial relations, in principle, requires bounding-box annotations, which are costly to gather. Moreover, it is not clear how to generalize spatial relations to unseen interactions. We address these challenges by developing a cross-attention mechanism that localizes objects from action locations and vice versa by predicting displacements between them, referred to as relational directions. During training, we estimate the relational directions as ones maximizing the scores of ground-truth interactions that guide predictions toward compatible action-object regions. By extensive experiments, we show the effectiveness of our framework, where we improve the state of the art by 2.6% mAP score and 5.8% recall score on HICO and Visual Genome datasets, respectively. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
“Erase/rewind”: How transgender Twitter discourses challenge and (re)politicize lesbian identities

Competing views on the in/compatibility of transgender status and lesbian identity is a source of conflict in the ongoing antagonism over transgender recognition. Many individuals with different transgender identities might lay claim to lesbian identity or lesbian discourse(s) more generally. However, this inclusion has been disputed in some circles insofar as it is seen to challenge or contradict characteristics of lesbianism. This paper explores how transgender discourses might challenge and (re)politicize lesbianism and lesbian identities. Given that social media platforms concentrate minority communities in one space and can serve to exacerbate antagonism over identities, I focus in this paper specifically on the Twitter context. This paper uses corpus-informed critical discourse studies to explore how cognitive models of lesbianism are articulated in transgender Twitter discourse/s. Findings indicate that transgender Twitter users (re)articulate sociohistorical narratives in lesbian discourse/s. At the same time, however, they also challenge and (re)politicize the essentialism of sex and gender in relation to lesbian identity and social practice. Hence, transgender Twitter discourse/s reflect potential explanations for contesting transinclusion in lesbianism, which may serve to reinforce transexclusionary claims for retaining lesbianism's uniqueness as a female space and experience.
Cytology image analysis

In the last three chapters, we have discussed the working principle of different CNN models that are popularly used in the domain of pattern recognition and/or object classification. Most of those models are considerably successful in medical diagnosis. In this chapter, we consider those tools and help readers understand cytology image analysis. We mainly employ ResNet and InceptionNet, with and without transfer learning (including different optimization solutions).
Support Convolution of CNN with Compression Sparse Matrix Multiplication Flow in TVM

Recently, machine learning has been widely adopted in various scenarios, especially in edge devices. These edge devices, such as smartphones or IoT devices, are usually powered by limited batteries. Therefore, how to increase performance and achieve power savings become one of the critical issues during the development of deep learning frameworks. In the research efforts, there are numerous optimizations or methodologies developed to aim at improving CNN performance. In this paper, we focus on the convolution layer in CNN, which is one of the most computationally demanding operators in neural networks. Therefore, improving the convolution will contribute significantly to the entire model. We find the opportunities of sparse convolution, in which the certain matrices are with high sparsity. We proposed a flow in TVM, which provides a sparse convolution flow with weight pruning. In our flow, we maximize the sparsity by pruning certain weight and pertaining the model. With our proposed flow, TVM model runtime could achieve 11.42x speedup on average with ImageNet based models compared to the original flow.
Monogamy of entanglement measures based on fidelity in multiqubit systems

We establish a connection between the tangle and entanglement measures based on fidelity, which yields easily computable upper and lower bounds of entanglement measures based on fidelity. By using this connection, we show exactly that the $$\alpha $$ th power of Bures measure of entanglement and geometric measure of entanglement, as special cases of entanglement measures based on fidelity, obeys a class of general monogamy inequalities in an arbitrary multiqubit mixed state for $$\alpha \ge 1$$ . Furthermore, by the virtue of these monogamy inequalities, we construct a kind of indicator, which can detect the genuine multiqubit entangled states even in the case that n-tangle loses its efficacy.
More Efficient Amortization of Exact Zero-Knowledge Proofs for LWE

We propose a practical zero-knowledge proof system for proving knowledge of short solutions \(\mathbf {s},\mathbf {e}\) to linear relations \(\mathbf {A}\mathbf {s}+\mathbf {e}=\mathbf {u}\pmod q\) which gives the most efficient solution for two naturally-occurring classes of problems. The first is when \(\mathbf {A}\) is very “tall”, which corresponds to a large number of LWE instances that use the same secret \(\mathbf {s}\). In this case, we show that the proof size is independent of the height of the matrix (and thus the length of the error vector \(\mathbf {e}\)) and rather only linearly depends on the length of \(\mathbf {s}\). The second case is when \(\mathbf {A}\) is of the form \(\mathbf {I} \otimes \mathbf {A}'\), which corresponds to proving many LWE instances (with different secrets) that use the same samples \(\mathbf {A}'\). The length of this second proof is square root in the length of \(\mathbf {s}\), which corresponds to a square root of the length of all the secrets. Our constructions combine recent advances in “purely” lattice-based zero-knowledge proofs with the Reed-Solomon proximity testing ideas present in some generic zero-knowledge proof systems – with the main difference that the latter are applied directly to lattice instances without going through intermediate problems.
Deep Learning-Based COVID-19 Twitter Analysis

Since the outbreak of COVID-19, the pandemic has impacted billions of people's lives around the world. Social media, such as Twitter, has been one of the major platforms where people express their emotions and thoughts about the unprecedented pandemic. In this paper, we perform Twitter sentiment analysis to gain insights into the development of Twitter users' sentiments during the period from February 1 to December 31, 2020. We use Long Short-term Memory (LSTM), a deep learning-based Natural Language Processing (NLP) method, to detect multiple sentiments out of eleven kinds. We also picked a number of topics of interest, such as social justice, mental health, vaccines, and misinformation, and conducted theme-specific sentiment analysis. In order to delve deeper into the meaning behind the sentiment trends, we used the Latent Dirichlet Allocation (LDA) algorithm to perform theme-specific topic modeling, which reveals interesting results.