Reviewerly/exHarmony huggingface dataset:
Enhancing Human Pose Estimation in Ancient Vase Paintings via Perceptually-grounded Style Transfer Learning

Human pose estimation (HPE) is a central part of understanding the visual narration and body movements of characters depicted in artwork collections, such as Greek vase paintings. Unfortunately, existing HPE methods do not generalise well across domains resulting in poorly recognised poses. Therefore, we propose a two step approach: (1) adapting a dataset of natural images of known person and pose annotations to the style of Greek vase paintings by means of image style-transfer. We introduce a perceptually-grounded style transfer training to enforce perceptual consistency. Then, we fine-tune the base model with this newly created dataset. We show that using style-transfer learning significantly improves the SOTA performance on unlabelled data by more than 6% mean average precision (mAP) as well as mean average recall (mAR) . (2) To improve the already strong results further, we created a small dataset (ClassArch) consisting of ancient Greek vase paintings from the 6–5th century BCE with person and pose annotations. We show that fine-tuning on this data with a style-transferred model improves the performance further. In a thorough ablation study, we give a targeted analysis of the influence of style intensities, revealing that the model learns generic domain styles. Additionally, we provide a pose-based image retrieval to demonstrate the effectiveness of our method. The code and pretrained models can be found at https://github.com/angelvillar96/STLPose .
Per-channel Quantization Level Allocation for Quantizing Convolutional Neural Networks

Deep convolutional neural network such as ResNet18/50 for ImageNet classification can be quantized into 3-bit or higher precision with the accuracy of full precision (FP32) baseline, yet 2-bit quantization incurs significant accuracy losses. In this work, we report, given that ranges of per-channel activation distributions vary across activation channels, 2-bit (4-level) quantization loses most of the information in the activation channel with a small range. To minimize the loss of information of the activation channel with a small range, we propose a novel quantization method called Per-channel Quantization Level Allocation (PCQLA), which quantizes activation into 2-bit precision with per-channel quantization clipping value according to the range of per-channel activation distribution. We also apply PCQLA mothed with outlier-aware quantization. On the ImageNet classification dataset, our method offers sufficient accuracies, comparable to the full precision baseline, with 2-bit activation quantization on ResNet-18/50.
Rough set reasoning using answer set programs

Reasoning about uncertainty is one of the main cornerstones of Knowledge Representation. Formal representations of uncertainty are numerous and highly varied due to different types of uncertainty intended to be modeled such as vagueness, imprecision and incompleteness. There is a rich body of theoretical results that has been generated for many of these approaches. It is often the case though, that pragmatic tools for reasoning with uncertainty lag behind this rich body of theoretical results. Rough set theory is one such approach for modeling incompleteness and imprecision based on indiscernibility and its generalizations. In this paper, we provide a pragmatic tool for constructively reasoning with generalized rough set approximations that is based on the use of Answer Set Programming (Asp). We provide an interpretation of answer sets as (generalized) approximations of crisp sets (when possible) and show how to use Asp solvers as a tool for reasoning about (generalized) rough set approximations situated in realistic knowledge bases. The paper includes generic Asp templates for doing this and also provides a case study showing how these techniques can be used to generate reducts for incomplete information systems. Complete, ready to run clingo Asp code is provided in the Appendix, for all programs considered. These can be executed for validation purposes in the clingo Asp solver.
Towards the Implementation of Arabic Language Mobile Apps Learning: Designed by User Insight

Arabic learning application is a learning application that is designed like a kind of quiz game with learning such as mastery of vocabulary, conversations, grammar and there is a material through the video that can be searched as needed, based on the results of preliminary questionnaires that have been conducted on 70 respondents stated 84,3% of difficulties in learning Arabic and 90% said that mastery of vocabulary, conversation and grammar is very difficult. The importance of the User Interface and User Experience in application development to make it easier for users to control/use the application, get responses from interactions and know the needs of users. In this study using the Goal Directed Design method, this method focuses on the needs of users with the stages passed, such as research, modelling, requirements, framework, and refinement. The results of this study are a design solution in Arabic learning applications that can be accepted by users with a SUS score of 79.68, which means acceptable to users in grade B by obtaining a good range value.
CURE: Curvature Regularization for Missing Data Recovery

Missing data recovery is an important and yet challenging problem in imaging and data science. Successful models often adopt certain carefully chosen regularization. Recently, the low dimensional manifold model (LDMM) was introduced by [S. Osher, Z. Shi, and W. Zhu, Low Dimensional Manifold Model for Image Processing, Technical report, cam report 16-04, UCLA, Los Angeles, CA, 2016] and shown to be effective in image inpainting. The authors of [Low Dimensional Manifold Model for Image Processing, Technical report, cam report 16-04, UCLA, Los Angeles, CA, 2016] observed that enforcing low dimensionality on the image patch manifold serves as a good image regularizer. In this paper, we observe that having only the low dimensional manifold regularization is not enough sometimes, and we need smoothness as well. For that, we introduce a new regularization by combining the low dimensional manifold regularization with a higher order \bf CUrvature \bf REgularization, which we call new regularization CURE for short. The key step of CURE is to solve a biharmonic equation on a manifold. We further introduce a weighted version of CURE, called WeCURE, in a similar manner as the weighted nonlocal Laplacian (WNLL) method [Z. Shi, S. Osher, and W. Zhu, Weighted nonlocal Laplacian on interpolation from sparse data, J. Sci. Comput., 73 (2017), pp. 1164--1177]. Numerical experiments for image inpainting and semisupervised learning show that the proposed CURE and WeCURE significantly outperform LDMM and WNLL, respectively.
Splitting subspaces of linear operators over finite fields

Let V be a vector space of dimension N over the finite field Fq and T be a linear operator on V. Given an integer m that divides N, an m-dimensional subspace W of V is T-splitting if V=W⊕TW⊕⋯⊕Td−1W where d=N/m. Let σ(m,d;T) denote the number of m-dimensional T-splitting subspaces. Determining σ(m,d;T) for an arbitrary operator T is an open problem. We prove that σ(m,d;T) depends only on the similarity class type of T and give an explicit formula in the special case where T is cyclic and nilpotent. Denote by σq(m,d;τ) the number of m-dimensional splitting subspaces for a linear operator of similarity class type τ over an Fq-vector space of dimension md. For fixed values of m,d and τ, we show that σq(m,d;τ) is a polynomial in q.
Grassland resource evaluation based on improved bp network model and analytic hierarchy process

Grassland resources are an important part of land resources. Moreover, it has the functions of regulating the climate, windproof and sand fixation, conserving water sources, maintaining water and soil, raising livestock, providing food, purifying the air, and beautifying the environment in terrestrial ecosystems. Grassland resource evaluation is of great significance to the sustainable development of grassland resources. Therefore, this paper improves the BP neural network, uses the comprehensive index method to calculate the weights in the analytic hierarchy process, and constructs a water resources carrying capacity research and analysis system based on the entropy weight extension decision theory. Meanwhile, this paper analyzes different levels of resource and environmental carrying capacity to achieve the purpose of comprehensive evaluation of resource and environmental carrying capacity. In addition, based on the theory of sustainable development, under the guidance of the principle of index system construction, this paper studies the actual situation of grassland resources and the availability and operability of data, and combines with the opinions given by experts to form an evaluation index system of grassland resources and environmental carrying capacity. Finally, through the actual case study analysis, it is concluded that the model constructed in this paper has a certain effect.
Cyber risk ordering with rank-based statistical models

Abstract In a world that is increasingly connected on-line, cyber risks become critical. Cyber risk management is very difficult, as cyber loss data are typically not disclosed. To mitigate the reputational risks associated with their disclosure, loss data may be collected in terms of ordered severity levels. However, to date, there are no risk models for ordinal cyber data. We fill the gap, proposing a rank-based statistical model aimed at predicting the severity levels of cyber risks. The application of our approach to a real-world case shows that the proposed models are, while statistically sound, simple to implement and interpret.
Argumentation-Based Health Information Systems: A Design Methodology

In this article, we present a design methodology for argumentation-based health information systems.With a focus on the application of formal argumentation, the methodology aims at eliciting requirements in regard to argumentation reasoning behavior, knowledge and user models, and business logic on levels below and above the argumentation layer.We highlight specific considerations that need to be made dependent on the system type, i.e., for clinical decision-support systems, patient-facing systems, and administration systems.In addition, we outline challenges in regard to the design of argumentation-based intelligent systems for healthcare, considering the state of the art of argumentation research, health information systems, and software design methods.For each challenge, we outline a mitigation strategy.
Fast Detection of Duplicate Bug Reports using LDA-based Topic Modeling and Classification

A bug tracking system continuously monitors the status of a software environment, like an Operating System (OS) or a user application. Whenever it detects an anomaly situation, it generates a bug report and sends it to the software developer or maintenance center. However, the newly reported bug can be an already existing issue that was reported earlier and may have a solution in the master report repository. This condition brings an avalanche of duplicate bug reports, posing a big challenge to the software development life cycle. Thus, early detection of duplicate bug reports has become an extremely important task in the software industry. To address this issue, this work proposes a double-tier approach using clustering and classification, whereby it exploits Latent Dirichlet Allocation (LDA) for topic-based clustering, multimodal text representation using Word2Vec (W2V), FastText (FT) and Global Vectors for Word Representation (GloVe), and a unified text similarity measure using Cosine and Euclidean metrics. The proposed model is tested on the Eclipse dataset consisting over 80,000 bug reports, which is the amalgamation of both master and duplicate reports. This work considers only the description of the reports for detecting duplicates. The experimental results show that the proposed two-tier model achieves a recall rate of 67% for Top-N recommendations with 3 times faster computation than the conventional one-on-one classification model.
Accurate Arbitrary-Shaped Scene Text Detection via Iterative Polynomial Parameter Regression

A number of scene text in natural images have irregular shapes which often cause significant difficulties for a text detector. In this paper, we propose a robust scene text detection method based on a parameterized shape modeling and regression scheme for text with arbitrary shapes. The shape model geometrically depicts a text region with a polynomial centerline and a series of width cues to capture global shape characteristics (e.g.. smoothness) and local shapes of the text respectively for accurate text localization, which differs from previous text region modeling schemes based on discrete boundary points or pixels. We further propose a text detection network PolyPRNet equipped with an iterative regression module for text’s shape parameters, which effectively enhances the detection accuracy of arbitrary-shaped text. Our method achieves state-of-the-art text detection results on several standard benchmarks.
Eliminating products’ fake reviews using network parameters and geo location

Abstract People purchase things based on the online reviews. But the reviews may not be trusted always. Sometimes there may be false information about the product and this may lead to loss for the sales. Customers also take wrong decision for purchasing the things. So, a system is proposed in this paper to eliminate false reviews. The product reviews are compared here. Using network parameters and geo location, the system identifies the IP address for PC and browser ID for mobile OS of the false review. Also it directs the admin to remove the review if it is attempted many times. By comparing the reviews, the level of the product can be increased. With the key boards the model divides the positive and negative reviews.
Identifying Key Classes Algorithm in Directed Weighted Class Interaction Network Based on the Structure Entropy Weighted LeaderRank

Identifying key classes can help software maintainers quickly understand software systems. The existing key class recognition algorithms consider the weight of class interaction, but the weight mechanism is single or arbitrary. In this paper, the multitype weighting mechanism is considered, and the key classes are accurately identified by using four kinds of interaction. By abstracting the software system into the directed weighted class interaction network, a novel Structure Entropy Weighted LeaderRank of identifying key classes algorithm is proposed. First, considering multiple types and directions of interactions between every pair of classes, the directed weighted class interaction software network (DWCIS-Network) is built. Second, Class Entropy of each class is initialized by the software structural entropy in DWCIS-Network; the Structure Entropy Weighted LeaderRank applies the biased random walk process to iterate Class Entropy. Finally, the iteration is completed to obtain the Final Class Entropy (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mtext>FCE</mml:mtext></mml:math>) of each class as the importance score of each class, top-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi>k</mml:mi></mml:math> classes are obtained, and key classes are identified. For two sets of experiments on Ant and JHotDraw, our approach effectively identifies key classes in class-level software networks for different top-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mi>k</mml:mi></mml:math> of classes, and the recall rates of our approach are the highest, 80% and 100%, respectively. From top-15% to top-5%, the precision of our approach is improved by 13.39%, which is the highest in comparison with the precisions of the other two classical approaches. Compared with the best performance of the two classical approaches, the RankingScore of our approach is improved by 16.51% in JHotDraw.
An IoT Based Obstacle Avoidance Robot Using Ultrasonic Sensor and Arduino

Abstract With the advance of technology in term of speed and modularity, the automation of robotic system comes into reality. In this paper an obstacle detection robot system explained for different purposes and applications. The ultra sonicrandrinfraredrsensors are actualized to distinguish obstacles on the robot’s way by imparting signsrtoranrinterfacedrmicrocontroller. The miniature regulator diverts therrobotrtormovera substitute way by inciting the motors in request to keep away from the distinguished obstacle. The exhibition assessment of the framework shows an exactness of 85 percentage and 0.15 likelihood of disappointment individually. Taking everything into account, an obstacle discovery circuit was effectively actualized utilizing the infrared and ultrasonic sensors that were mounted on the panel.
Analysis of Web Security Using Open Web Application Security Project 10

Open Web Application Security Project 10 is a web application security testing framework method that focuses on web application security to find weaknesses in a website. The Open Web Application Security Project 10 aims to ensure the safety of websites in form checklists. Open Web Application Security Project 10 has the ten most dangerous types of website vulnerabilities such as injection, broken authentication, sensitive data exposure, Extensible Markup Language external entities, corrupted access control, security misconfiguration, cross-site scripting, unreliable deserialization, segment exploitation with known weaknesses, and lack of logging and checking. This paper analyzes and tests the security of the web along with six sub-domains with the aim of knowing and assessing the security level of a website, whether additional security is needed, and recommendations on the website. The results of this paper indicate that the web has a security level 80%, web informatics engineering subdomain 60%, information systems 60%, informatics management 60%, integrated academic system 80%, student acceptance 80% and e-learning 80%.
ALBERT-Based Self-Ensemble Model With Semisupervised Learning and Data Augmentation for Clinical Semantic Textual Similarity Calculation: Algorithm Validation Study

Background In recent years, with increases in the amount of information available and the importance of information screening, increased attention has been paid to the calculation of textual semantic similarity. In the field of medicine, electronic medical records and medical research documents have become important data resources for clinical research. Medical textual semantic similarity calculation has become an urgent problem to be solved. Objective This research aims to solve 2 problems—(1) when the size of medical data sets is small, leading to insufficient learning with understanding of the models and (2) when information is lost in the process of long-distance propagation, causing the models to be unable to grasp key information. Methods This paper combines a text data augmentation method and a self-ensemble ALBERT model under semisupervised learning to perform clinical textual semantic similarity calculations. Results Compared with the methods in the 2019 National Natural Language Processing Clinical Challenges Open Health Natural Language Processing shared task Track on Clinical Semantic Textual Similarity, our method surpasses the best result by 2 percentage points and achieves a Pearson correlation coefficient of 0.92. Conclusions When the size of medical data set is small, data augmentation can increase the size of the data set and improved semisupervised learning can boost the learning efficiency of the model. Additionally, self-ensemble methods improve the model performance. Our method had excellent performance and has great potential to improve related medical problems.
Using Abstract Contracts for Verifying Evolving Features and Their Interactions

Today, software systems are rarely developed monolithically, but may be composed of numerous individually developed features. Their modularization facilitates independent development and verification. While feature-based strategies to verify features in isolation have existed for years, they cannot address interactions between features. The problem with feature interactions is that they are typically unknown and may involve any subset of the features. Contrary, a family-based verification strategy captures feature interactions, but does not scale well when features evolve frequently. To the best of our knowledge, there currently exists no approach with focus on evolving features that combines both strategies and aims at eliminating their respective drawbacks. To fill this gap, we introduce Fefalution, a feature-family-based verification approach based on abstract contracts to verify evolving features and their interactions. Fefalution builds partial proofs for each evolving feature and then reuses the resulting partial proofs in verifying feature interactions, yielding a full verification of the complete software system. Moreover, to investigate whether a combination of both strategies is fruitful, we present the first empirical study for the verification of evolving features implemented by means of feature-oriented programming and by comparing Fefalution with another five family-based approaches varying in a set of optimizations. Our results indicate that partial proofs based on abstract contracts exhibit huge reuse potential, but also come with a substantial overhead for smaller evolution scenarios.
Convolutional Neural Network based Handwritten Devanagari Character Recognition

Industry 4.0 might be thumping at our door, but there are millions of individuals who are still disconnected from even the most fundamental technologies. One of the major reasons for this disparity is the language gap. The majority of them don't know English, which is the de-facto language for most of the technologies. Only 12.6% of all Indians speak English according to the 2011 census. Language is the medium through which an individual expresses himself or herself. Education of the rural section of society is one of the most important things to accomplish if we aim to develop the country. Because of this, there is a need for a reliable software solution for recognizing the traditional scripts. This would also be helpful for the purpose of remote schooling in times of lockdown and quarantine.The paper proposes a methodology for recognition of handwritten `Devanagari' characters. Devanagari being one of the most common scripts, is used in many Indian dialects. The Hindi language is also written in the Devanagari script as shown in Fig 1. This paper explores and analyzes the use of Deep learning techniques such as the Convolutional Neural Networks for the recognition of Devanagari characters. Inspired by the structure of the brain, CNNs classify characters by making use of neurons linked in various layers so as to achieve maximum efficiency. In this paper, 6 layers of neurons were used for the purpose of classifying Devanagari characters. An accuracy of 95.6% was achieved in this approach. Once recognized, the handwritten Devanagari characters can easily be translated into English or any other languages.
A Survey of Real-Time Social-Based Traffic Detection

Online traffic news web sites do not always announce traffic events in areas in real-time. There is a capability to employ text mining and machine learning techniques on the Twitter stream to perform event detection, to develop a real-time traffic detection system. In this present survey paper, we deliberate the current state-of-art techniques in detecting traffic events in real-time focusing on seven papers [1], [2], [3], [4], [5], [6], [7]. Lastly, applying effective text mining techniques and the SVM classifier in paper [2] gave the best results (i.e., 95.75% accuracy and 95.8% F1-score).