Reviewerly/exHarmony huggingface dataset:
CBCS - Comic Book Cover Synopsis: Generating Synopsis of a Comic Book with Unsupervised Abstractive Dialogue

Document analysis which aims at understanding the semantics of the document is an active field of research in today's world. Growth of graphical novels such as comics is magnificent, by incorporating digitized and digital-born comics. This enables a machine intelligence to recognize the key elements present in a comic book story. Analyzing comic story book is complex as they contains text drawings, balloons embedded in a comic page and these elements are considered as key components of a comic The transition in digital era has made the people to have a different approach in reading out the books through comics and with the world moving towards automation, the proposed work incorporated deep learning approaches to analyze the key components contained in comics. This work aimed to design a model to automatically learn to extract the comic specific components and further identification of text has been done using pre-trained language recognition models. The main objective of the proposed work is to have quick and crisp understanding of a comic story. Hence, the proposed work designed to generate the unsupervised abstractive dialogues representing the whole story without losing the essence of it. The proposed work has attained an improved performance over traditional method for generating the abstractive summarized story and state-of-the-art method for comic component detection.
Allowing mutations in maximal matches boosts genome compression performance

Abstract Motivation A maximal match between two genomes is a contiguous non-extendable sub-sequence common in the two genomes. DNA bases mutate very often from the genome of one individual to another. When a mutation occurs in a maximal match, it breaks the maximal match into shorter match segments. The coding cost using these broken segments for reference-based genome compression is much higher than that of using the maximal match which is allowed to contain mutations. Results We present memRGC, a novel reference-based genome compression algorithm that leverages mutation-containing matches (MCMs) for genome encoding. MemRGC detects maximal matches between two genomes using a coprime double-window k-mer sampling search scheme, the method then extends these matches to cover mismatches (mutations) and their neighbouring maximal matches to form long and MCMs. Experiments reveal that memRGC boosts the compression performance by an average of 27% in reference-based genome compression. MemRGC is also better than the best state-of-the-art methods on all of the benchmark datasets, sometimes better by 50%. Moreover, memRGC uses much less memory and de-compression resources, while providing comparable compression speed. These advantages are of significant benefits to genome data storage and transmission. Availability and implementation https://github.com/yuansliu/memRGC. Supplementary information Supplementary data are available at Bioinformatics online.
SUW-Learn: Joint Supervised, Unsupervised, Weakly Supervised Deep Learning for Monocular Depth Estimation

We introduce SUW-Learn: A framework for deep-learning with joint supervised learning (S), unsupervised learning (U), and weakly-supervised learning (W). We deploy SUWLearn for deep learning of the monocular depth from images and video sequences. The supervised learning module optimizes a depth estimation network by knowledge of the ground-truth depth. In contrast, the unsupervised learning module has no knowledge of the ground-truth depth, but optimizes the depth estimation network by predicting the current frame from the estimated 3D geometry. The weakly supervised module optimizes the depth estimation by evaluating the consistency between the estimated depth and weak labels derived from other information, such as the semantic information. SUW-Learn trains the deep-learning networks end-to-end with joint optimization of the desired SUW objectives. We benchmark SUW-Learn on the commonly-used KITTI driving-scene and achieve the state-of-the-art performance. To demonstrate the capacity of SUW-Learn in learning the depth of scenes with people from different sources with different domain knowledge, we construct the M&M dataset from the Megadepth and Mannequin Challenge datasets.
Simultaneous Vehicle Detection and Classification Model based on Deep YOLO Networks

Due to the rapid growth of vehicles, traffic monitoring and tracking systems in the last decade, vehicle detection and extracting information such as vehicle type and model and car plate recognition are the important issues of the day and many efforts have been made using different methods to identify vehicles. Given the high number of vehicles and similarities of classes, it is a difficult task to find an accurate and rapid approach to differentiate available classes and classify them. In this article we propose a new approach which simultaneously detects vehicles and identifies their type. Two models are trained in this paper. The first model is based on CNN networks to extract features and detect vehicle models, and the second model which is the main contribution of this article is based on YOLO (You Only Look Once) algorithm and SSD to detect vehicle location in the image. To train and test this approach, we collect 150,000 images of 115 domestic and foreign vehicle classes from Iranian websites. Hence, the images have large variations in image size, illumination and pose. The experimental results on the accrued dataset shows that the proposed method is able to correctly classify 91% of the vehicles in uncontrolled conditions.
Quantum and classical bounds for two-state overlaps

Suppose we have $N$ quantum systems in unknown states $\left|{\ensuremath{\psi}}_{i}\right\ensuremath{\rangle}$, but we know the value of some pairwise overlaps ${\left|\ensuremath{\langle}{\ensuremath{\psi}}_{k}|{\ensuremath{\psi}}_{l}\ensuremath{\rangle}\right|}^{2}$. What can we say about the values of the unknown overlaps? We provide a complete answer to this problem for three pure states and two given overlaps and a way to obtain bounds for the general case. We discuss how the answer contrasts from that of a classical model featuring only coherence-free, diagonal states, and we describe three applications: basis-independent coherence witnesses, dimension witnesses, and characterization of multiphoton indistinguishability.
Effective Comparison of LDA with LSA for Topic Modelling

The process of converting unstructured data into a structured readable format is becoming hard day by day. Till day every organization consists of more than 80% of its operational data in an unreadable format. The proposed method helps in converting unreadable data to a readable structured format with the help of Machine learning were classification, and clustering plays a crucial role in converting the operational data into data models and visualize the processed information to the end-user. As organizations have specific requirements, considering them, we are going to implement latent dirichlet allocation (LDA) and latent semantic analysis (LSA), which were able to handle discrete data. Also, a comparison is made to test divergence, throughput, quality, and response time, as both of them can classify the data based on the content and by giving labels to each category. The algorithm with better divergence is implemented that can handle the organizational requirements by presenting the top areas that need to improve/concentrate depending on the analytics made by the algorithm on the available discrete data, and by implementing visualization techniques, the results will be even displayed in graphical format.
An Exploration of African-American Pregnant Women's Information-Seeking Behavior in Detroit

In the United States, African-American women are three times more likely to die of pregnancy-related issues, and 57% more likely to have miscarriages than White women. In part, this is due to deficiencies in the information ecology serving pregnant mothers. We report on a qualitative study with 16 low-income, African-American, pregnant women from the Detroit area, focused on understanding their informational needs and information-seeking behavior. We find that our participants consumed information from multiple sources voraciously; treated formal medical sources as no more trustworthy than other information sources; sought corroborative evidence when making decisions; relied on video and social media sources; and voiced requests for material and social support more than for medical information. Among our recommendations are increased use of video as a means for pregnancy-related information dissemination -- particularly testimonials by experienced mothers with similar backgrounds -- and better integration of medically sound voices in spaces where mothers already congregate online.
An efficient Split-Merge re-start for the K-means algorithm

The <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -means algorithm is one of the most popular clustering methods. However, it is a well-known fact that its performance, in terms of quality of the obtained solution and computational load, highly depends upon its initialization phase. For this reason, different initialization techniques have been developed throughout the years to enable its fast convergence to competitive solutions. In this sense, it is common practice to re-start the <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -means algorithm several times via one of these techniques and keep the solution with the lowest error. Unfortunately, such a choice is still likely to be a poor approximation of the optimal set of centroids. In this article, we introduce a cheap Split-Merge step that can be used to re-start the <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -means algorithm after reaching a fixed point. Under some settings, one can show that this approach reduces the error of the given fixed point without requiring any further iteration of the <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -means algorithm. Moreover, experimental results show that this strategy is able to generate approximations with an associated error that is hard to reach for different multi-start methods, such as multi-start Forgy <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -means, <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -means++ and Hartigan <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -means, while also computing a lower amount of distances than the previous algorithms.
Rapid Development of Competitive Translation Engines for Access to Multilingual COVID-19 Information

Every day, more people are becoming infected and dying from exposure to COVID-19. Some countries in Europe like Spain, France, the UK and Italy have suffered particularly badly from the virus. Others such as Germany appear to have coped extremely well. Both health professionals and the general public are keen to receive up-to-date information on the effects of the virus, as well as treatments that have proven to be effective. In cases where language is a barrier to access of pertinent information, machine translation (MT) may help people assimilate information published in different languages. Our MT systems trained on COVID-19 data are freely available for anyone to use to help translate information (such as promoting good practice for symptom identification, prevention, and treatment) published in German, French, Italian, Spanish into English, as well as the reverse direction.
Compressed Image Restoration via Artifacts-Free PCA Basis Learning and Adaptive Sparse Modeling

Visually unpleasant compression artifacts frequently appear in block-based transform coding, especially at low bit rates. This paper presents a new artifact reduction scheme based on Bayesian sparse modeling and artifacts-free PCA basis learning. To avoid the effect of blocking artifacts, we propose to learn artifacts-free PCA basis from clean images. We concatenate the clean patches and their compressed counterparts to learn paired distribution prior via the Gaussian Mixture Model (GMM). By this way, the GMM characterizes the mapping between the clean image and its compressed version. To restore a compressed patch, the best matched GMM component is assigned using the patch in the compressed image subspace. The artifacts-free PCA basis is obtained according to the mapping learned by the paired GMM. In practice, the statistical distributions of different sparse coefficients in different patches may dramatically vary with image contents. Instead of using a global zero-mean distribution for all coefficients, we propose to adaptively model the prior of each band in a Bayesian framework. The expectation and variance of each band are adaptively learned from the similar patches within the image. Thus, different transform bands are regularized unequally according to the learned priors. Experimental results show that the proposed scheme outperforms most of the compared schemes in terms of both objective quality and perceptual quality.
Detection of False Positive and False Negative Samples in Semantic Segmentation

In recent years, deep learning methods have outperformed other methods in image recognition. This has fostered imagination of potential application of deep learning technology including safety relevant applications like the interpretation of medical images or autonomous driving. The passage from assistance of a human decision maker to ever more automated systems however increases the need to properly handle the failure modes of deep learning modules. In this contribution, we review a set of techniques for the self-monitoring of machine-learning algorithms based on uncertainty quantification. In particular, we apply this to the task of semantic segmentation, where the machine learning algorithm decomposes an image according to semantic categories. We discuss false positive and false negative error modes at instance-level and review techniques for the detection of such errors that have been recently proposed by the authors. We also give an outlook on future research directions.
Precise detection of Chinese characters in historical documents with deep reinforcement learning

The decision-making ability of deep reinforcement learning has been proved successfully in a variety of fields. Here, we use this method for precise character detection by making tight bounding boxes around the Chinese characters in historical documents. An agent is trained to learn the control policy of fine-tuning a bounding box step-by-step through a Markov Decision Process. We introduce a novel fully convolutional network with position-sensitive Region-of-Interest (RoI) pooling (FCPN). The network receives character patches as input without fixed size, and it can fuse position information into the features of actions. Besides, we propose a dense reward function (DRF) that provides excellent rewards according to different actions and environment states, improving the decision-making ability of the agent. Our approach is designed as a universal method that can be applied to the output of all character-level or word-level text detectors to obtain more precise detection results. Application to the Tripitaka Koreana in Han (TKH) and Multiple Tripitaka in Han (MTH) datasets confirm the very promising performance of this method. In particular, our approach yields a significant improvement under a large Intersection over Union (IoU) of 0.8. The robustness and generality are also proved by experiments on the scene text datasets ICDAR2013 and ICDAR2015.
Analysis of Affine Motion-Compensated Prediction in Video Coding

Motion-compensated prediction is used in video coding standards like High Efficiency Video Coding (HEVC) as one key element of data compression. Commonly, a purely translational motion model is employed. In order to also cover non-translational motion types like rotation or scaling (zoom), e. g. contained in aerial video sequences such as captured from unmanned aerial vehicles (UAV), an affine motion model can be applied. In this work, a model for affine motion-compensated prediction in video coding is derived. Using the rate-distortion theory and the displacement estimation error caused by inaccurate affine motion parameter estimation, the minimum required bit rate for encoding the prediction error is determined. In this model, the affine transformation parameters are assumed to be affected by statistically independent estimation errors, which all follow a zero-mean Gaussian distributed probability density function (pdf). The joint pdf of the estimation errors is derived and transformed into the pdfof the location-dependent displacement estimation error in the image. The latter is related to the minimum required bit rate for encoding the prediction error. Similar to the derivations of the fully affine motion model, a four-parameter simplified affine model is investigated. Both models are of particular interest since they are considered for the upcoming video coding standard Versatile Video Coding (VVC) succeeding HEVC. Both models provide valuable information about the minimum bit rate for encoding the prediction error as a function of affine estimation accuracies.
SPAMI: A cognitive spam protector for advertisement malicious images

In modern era, the graphical information is presented in the form of web images. As the dependency of human beings on web information is increasing day-by-day, so the spammers are injecting spam by adopting new spamming techniques. Image spam is a spamming technique that integrates spam text contents into graphical images in order to bypass conventional text-based spam filters. The spam images are of various categories, such as redirection spam, advertisement spam, fake review, and content spam. In order to detect image spam efficiently, it is important to analyze the features of the image data. However, the existing image spam detection techniques in literature focused on textual or graphic features of the image. Moreover, to extract the relevant features from the images is also a challenging task. So, to fill these gaps, in this paper, we propose a Spam Protector for Advertisement of Malicious Images (SPAMI) framework using features extraction by browsing different websites and webpages. SPAMI is a cognitive spam protector which labels the spam advertisement images by using deep learning models. Three deep learning models are used for the same, i.e., CNN, RNN, and LSTM. The regress analysis of output from these models is done in the proposed SPAMI framework. Finally, we analysed the labels (Advertisement, Suspicious, Normal) for all the 600 images collected. The accuracy obtained from these models is 95% with real-time collected images, which improved up to 97% when tested with ”Image Spam Hunter” dataset.
Some innovations of teaching the course on Data structures and algorithms

The teaching of practical foundations of computer science is still a big challenge. The course on Data Structures and Algorithms is one of the most important foundational courses that are necessary to be included in the curriculum of future IT experts. The content of this course is quite wide and it differs in many universities. In our teaching process, we identified some interesting problems that could be presented more attractively - using the visualizing software. In this paper, we present a purpose, rôle, design and methodology of our developed software tool for the convenience of teaching and studying the problem of the longest common subsequence.
Play and rewind: Context-aware video temporal action proposals

In this paper, we investigate the problem of Temporal Action Proposal (TAP) generation, which plays a fundamental role in large-scale untrimmed video analysis but remains largely unsolved. Most of the prior works proposed the temporal actions by predicting the temporal boundaries or actionness scores of video units. Nevertheless, context information among surrounding video units has not been adequately explored, which may result in severe loss of information. In this work, we propose a context-aware temporal action proposal network which makes full use of the contextual information in two aspects: 1) To generate initial proposals, we design a Bi-directional Parallel LSTMs to extract the visual features of a video unit by considering its contextual information. Therefore, the prediction of temporal boundaries and actionness scores will be more accurate because it knows what happened in the past and what will happen in the future; and 2) To refine the initial proposals, we design an action-attention based re-ranking network which considers both surrounding proposal and initial actionness scores to assign true action proposals with high confidence scores. Extensive experiments are conducted on two challenging datasets for both temporal action proposal generation and detection tasks, demonstrating the effectiveness of the proposed approach. In particular, on THUMOS’14 dataset, our method significantly surpasses state-of-the-art methods by 7.73% on [email protected] Our code is released at: https://github.com/Rheelt/TAPG.
Two quantum Ising algorithms for the shortest-vector problem

Traditional public key cryptography will become obsolete when quantum computers are able to break it. The authors propose two quantum algorithms to solve the shortest-vector problem, which could play an important role in designing new cryptosystems for the postquantum era.
A machine learning based framework for code clone validation

A code clone is a pair of code fragments, within or between software systems that are similar. Since code clones often negatively impact the maintainability of a software system, several code clone detection techniques and tools have been proposed and studied over the last decade. However, the clone detection tools are not always perfect and their clone detection reports often contain a number of false positives or irrelevant clones from specific project management or user perspective. To detect all possible similar source code patterns in general, the clone detection tools work on the syntax level while lacking user-specific preferences. This often means the clones must be manually inspected before analysis in order to remove those false positives from consideration. This manual clone validation effort is very time-consuming and often error-prone, in particular for large-scale clone detection. In this paper, we propose a machine learning approach for automating the validation process. First, a training dataset is built by taking code clones from several clone detection tools for different subject systems and then manually validating those clones. Second, several features are extracted from those clones to train the machine learning model by the proposed approach. The trained algorithm is then used to automatically validate clones without human inspection. Thus the proposed approach can be used to remove the false positive clones from the detection results, automatically evaluate the precision of any clone detectors for any given set of datasets, evaluate existing clone benchmark datasets, or even be used to build new clone benchmarks and datasets with minimum effort. In an experiment with clones detected by several clone detectors in several different software systems, we found our approach has an accuracy of up to 87.4% when compared against the manual validation by multiple expert judges. The proposed method also shows better results in several comparative studies with the existing related approaches for clone classification.