Reviewerly/exHarmony huggingface dataset:
Rule-based Bayesian regression

We introduce a novel rule-based approach for handling regression problems. The new methodology carries elements from two frameworks: (i) it provides information about the uncertainty of the parameters of interest using Bayesian inference, and (ii) it allows the incorporation of expert knowledge through rule-based systems. The blending of those two different frameworks can be particularly beneficial for various domains (e.g., engineering), where even though the significance of uncertainty quantification motivates a Bayesian approach, there is no simple way to incorporate researcher intuition into the model. We validate our models by applying them to synthetic applications: a simple linear regression problem and two more complex structures based on partial differential equations, and we illustrate their use through two cases derived from real data. Finally, we review the advantages of our methodology, which include the simplicity of the implementation, the uncertainty reduction due to the added information and, in some occasions, the derivation of better point predictions, and we outline limitations, mainly from the computational complexity perspective, such as the difficulty in choosing an appropriate algorithm and the added computational burden.
Neuro-evolution using game-driven cultural algorithms

Contemporary 'deep learning' (DL) models have proven to be effective in a wide variety of applications. However, the right network topology for the problem at hand may be complex and not immediately obvious. This has given rise to the secondary field of neural architecture search (NAS). This paper describes a NAS method based on graph evolution pioneered by Neuro-evolution of Augmenting Topologies (NEAT), but driven by the evolutionary mechanisms underlying Cultural Algorithms (CA). CA is a population-based, stochastic optimization system inspired by problem solving in human cultures, suited to solving problems such as NAS. We present CATNeuro a system for evolving DL models guided by CA metaheuristics called Knowledge Sources (KS). The KS store knowledge harvested from prior generations and use it to guide subsequent generations in the search space. A knowledge distribution mechanism, which assigns a KS to each individual in the population, is an instrumental part of this process. CATNeuro, is applied to find optimal network topologies to play a 2D fighting game called FightingICE (based on "The Rumble Fish" game). A policy-based, reinforcement learning method is used to create the training data for network optimization. CATNeuro is still evolving. In this primary foray into NAS, we contrast the performance of CATNeuro with two different knowledge distribution mechanisms - the stalwart Weighted Majority (WTD) - which represents "wisdom of the crowds" - and a new one based on the Stag-Hunt game from evolutionary game theory. We show that Stag-Hunt has a statistical edge over WTD in many areas and thus is a better candidate for future development with CATNeuro.
A Novel Approach for SQL Injection Avoidance Using Two-Level Restricted Application Prevention (TRAP) Technique

Kumar, Anup Rai, Sandeep Boghey, RajeshCurrent IT world is moving forward in revolutionary terms of e-commerce, artificial intelligence, machine learning, and many more. A lot of change has been observed in technology stack for past 2–3 years. One of the notable advancement is evolution of e-commerce sites and various other sites where user input is required. This has made these sites more vulnerable to a type of attack termed as SQL injection attack. These are just SQL executable code passed through the inputs. SQL injection attacks are the most easiest and high impacting attacks on an application. There are several ways that these attacks work, namely appending true statement, modifying existing data, union query to pull whole data, and many more. These attacks have potential to take down an entire application or delete the critical information from database. Infinite loops can also be appended in the form on functions which severely affects whole application infrastructure. User input cannot be removed from Internet ecosystem as it is the basic need for a website. Given that, this is also the most exploited channel to attack the website. By going through most of the researches done in this area, it is observed that majority of preventive techniques either work in single tier or increase complexity of the system just to implement the technique. In this paper, we propose a two-level restricted application prevention (TRAP) technique for SQL injection prevention which leads to a robust and time efficient, two-tier defense system against SQL injections with comparatively minimal impact to the application.
A Roadmap for the GraphBLAS C++ API

The GraphBLAS are building blocks for expressing graph algorithms in terms of linear algebra. Currently, the GraphBLAS are defined as a C API. Implementations of the GraphBLAS have exposed limitations in expressiveness and performance due to limitations in C. A move to C++ should address many of these limitations while providing a simpler API. Furthermore, for methods based on user-defined types and operators, the performance should be significantly better. C++ has grown into a pervasive programming language across many domains. We see a compelling argument to define a GraphBLAS C++ API. This paper presents our roadmap for the development of a GraphBLAS C++ API. Open issues are highlighted with the goal of fostering discussion and generating feedback within the GraphBLAS user community to guide us as we develop the GraphBLAS C++ API.
A Neural-based Architecture For Small Datasets Classification

Digital Libraries benefit from the use of text classification strategies since they are enablers for performing many document management tasks like Information Retrieval. The effectiveness of such classification strategies depends on the amount of available data and the classifier used. The former leads to the design of data augmentation solutions where new samples are generated into small datasets based on the semantic similarity between existing samples and concepts defined within external linguistic resources. The latter relates to the capability of finding, which is the best learning principle to adopt for designing an effective classification strategy suitable for the problem. In this work, we propose a neural-based architecture thought for addressing the text classification problem on small datasets. Our architecture is based on BERT equipped with one further layer using the sigmoid function. The hypothesis we want to verify is that by using embeddings learned by a BERT-based architecture, one can perform effective classification on small datasets without the use of data augmentation strategies. We observed improvements up to 14% in the accuracy and up to $23%$ in the f-score with respect to baseline classifiers exploiting data augmentation.
IntroVAC: Introspective Variational Classifiers for learning interpretable latent subspaces

Learning useful representations of complex data has been the subject of extensive research for many years. In particular, with the diffusion of complex Deep Learning-based approaches in engineering applications, the possibility to interpret, to a certain degree, model predictions is of fundamental importance for both the model users and developers. In the context of Deep Neural Networks, Variational Autoencoders have gained lots of attention since they provide an explicit model of the data distribution based on an encoder/decoder architecture which is able to both generate images and encode them in a low-dimensional subspace. However, the latent space is not easily interpretable and the generation capabilities show some limitations since images typically look blurry and lack details. In this paper, we propose the Introspective Variational Classifier (IntroVAC), a model that learns interpretable latent subspaces by exploiting information from an additional label and provides improved image quality thanks to an adversarial training strategy. We show that IntroVAC is able to learn meaningful directions in the latent space enabling fine-grained manipulation of image attributes. We validated our approach on the CelebA dataset. When compared with standard Variational Autoencoder Classifiers, the proposed approach outperform them by achieving a Frechét Inception Distance of 25.5 versus a value of 63.9.
Classification Using Decision Tree Ensembles

Across disciplines, researchers and practitioners employ decision tree ensembles such as random forests and XGBoost with great success. What explains their popularity? This chapter showcases how marketing scholars and decision-makers can harness the power of decision tree ensembles for academic and practical applications. The author discusses the origin of decision tree ensembles, explains their theoretical underpinnings, and illustrates them empirically using a real-world telemarketing case, with the objective of predicting customer conversions. Readers unfamiliar with decision tree ensembles will learn to appreciate them for their versatility, competitive accuracy, ease of application, and computational efficiency and will gain a comprehensive understanding why decision tree ensembles contribute to every data scientist's methodological toolbox.
Arabic Artistic Script Style Identification Using Texture Descriptors.

Texture descriptors have been widely used for many computer vision tasks. Document Analysis (DA) is a hot topic of research, which aims at analyzing digitized documents containing different types of texts. In this paper, we design and evaluate a system for Arabic artistic style recognition using numerous texture descriptors. These descriptors have been chosen based on their effectiveness and the high performance they showed in other related works. To make it more comprehensive, the system has been evaluated using five different classifiers. Results indicated that texture descriptors appropriately fit the task and yielded almost perfect performances for most of the styles.
CSAA: An Online Annotating Platform for Classifying Sections of Academic Articles

As the basic work of knowledge mining and service based on full-text of articles, recognizing the categories of section in academic articles can help us to understand the function of content in different parts of the article. There is no existing a large-scale annotated corpus of section categories which can be used to classify the sections of the articles via machine learning. This study developed an annotating platform, namely CASS to implement the functions, including grouping annotators, task assignment, online tagging and doubtful corpus review. It can improve the tagging efficiency and provide convenience for task management, data preservation, results review and verifying inconsistent results.
Hybrid Data-Level Techniques for Class Imbalance Problem

In data mining, the task of classification is to identify an instance in a dataset into one of the predefined classes. In real-life applications, the traditional classification does not work well for imbalanced datasets, i.e., where one class contains very few number of data points, named as the minority class, as compared to other class(es), named as the majority class(es). This problem of imbalanced dataset distribution is termed as the class imbalance problem (CIP). To solve CIP, the researchers examined the effects of CIP on the performance of classifier and proposed various techniques to handle this problem. In literature, these techniques are majorly classified into three levels: data-level approaches (or pre-processing techniques), algorithm-level approaches and ensemble-level approaches. The sampling-based approaches are further subdivided into three categories, such as oversampling techniques, undersampling techniques and hybrid sampling (undersampling + oversampling) techniques. In this paper, we proposed three hybrid sampling techniques (named as Bor-SMOTE+TL, TL+C-SMOTE, SL-SMOTE+TL) using Tomek links (an undersampling) technique combined with the oversampling techniques. The experiments are carried out using real-life imbalanced datasets to show the usefulness of the proposed techniques as compared to the existing sampling techniques.
Structural Similarity Measurement Based Cost Function for Stereo Matching of Automotive Applications

The human visual perception uses structural information to recognize stereo correspondences in natural scenes. Therefore, structural information is important to build an efficient stereo matching algorithm. In this paper, we demonstrate that incorporating the structural information similarity, extracted either from image intensity (SSIM) directly or from image gradients (GSSIM), between two patches can accurately describe the patch structures and, thus, provides more reliable initial cost values. We also address one of the major phenomenons faced in stereo matching for real world scenes, radiometric changes. The performance of the proposed cost functions was evaluated within two stages: the first one considers these costs without aggregation process while the second stage uses the fast adaptive aggregation technique. The experiments were conducted on the real road traffic scenes KITTI 2012 and KITTI 2015 benchmarks. The obtained results demonstrate the potential merits of the proposed stereo similarity measurements under radiometric changes.
A Knot Selection Algorithm for Splines in Logistic Regression

In ordinary logistic regression, the logit of the conditional probability of the response given the covariates is modelled as a linear function of the covariates. In this study, a more general logistic regression model is considered, where linearity is not assumed. Since the linear function of the covariates is replaced by a general function of the covariates, spline approximation is used. A knot selection algorithm is proposed to determine the knot locations in spline approximation. Simulation experiments have been carried out to check the performance of the proposed algorithm. The proposed algorithm performs reasonably well.
AI planning applied to CIS-based disaster response

GIS information is clearly critical for disaster response planning. Formal planning can eliminate otherwise unanticipated conflicts in disaster response planning, particularly in distributed planning. Such conflicts may result in execution-time discovery and resolution, with sub-optimal results. This is especially true in conflicts based upon GIS information as the cases may be subtle, typically resulting from lack of resource access. However, GIS systems do not produce the kind of quantitative information needed for an interface with many other systems, especially planning. New technologies such as GeoSPARQL provide some hope but much work remains. This is a fertile area for research and development.
Evolving sqrt into 1/ <i>x</i> via software data maintenance

While most software automation research concentrates on programs' code, we have started investigating if Genetic Improvement (GI) of data can assist developers by automating aspects of the maintenance of parameters embedded in source code. We extend recent GI work on optimising compile time constants to give new functionality and describe the transformation of a GNU C library square root function into the double precision reciprocal function, drcp. Multiplying by 1/x (drcp) allows division free division without requiring the hardware to support division. The evolution (6 seconds) and indeed the GI dp division (7.14 ± 0.012 nS) are both surprisingly fast.
CLAIM: An Enhanced Machine Learning Technique for Discrepancy Report Analysis

CLAIM is a tool for analyzing and classifying discrepancy reports that allows for the incorporation of domain expert knowledge into a semi-supervised machine learning (ML) process (a semi-supervised learning uses a small number of manually labeled data and a much larger amount of unlabeled for training a machine learning algorithm). By using this domain knowledge, classification accuracy is higher than conventional ML approaches. The advantages are particularly apparent with small, imbalanced data sets that are quite common in discrepancy report data sets (an imbalanced data set has an unequal distribution of documents categories within each category). The CLAIM method is robust against human bias and can tolerate misclassifications of up to 20% of the training set. The increased accuracy of the CLAIM methodology makes ML a viable tool for safety, reliability, and software development process decision making. The modest human labor requirement enables use of the method under circumstances that previously made free text discrepancy report analysis infeasible due to resource, scheduling, and cost constraints.
A Survey on Partially Occluded Faces

Over the past decade, partially occluded face recognition has been an urgent challenge to computer visionaries due to conditions, which appear unconstrained. The main aim of the facial recognition system is to attain the ability to detect partially occluded regions of an individual’s face and authenticating/verifying that face. There are existing neural networks that are proven to be perfect on analysing the patterns for constrained looks but fail to perform in analysing partially occluded faces that are common in the real world. The paper discusses the trainable Deep Learning Neural Network (DLNN) for partially occluded faces by recognizing all the possible faces in the image, either resting, posing or projecting faces and matching them across the trained datasets of DLNN and encoding the identified faces.
Media Resources: As An Effective Tool In Modern Education

The article substantiates the new role of media resources in modern education.According to the author, the use of media resources in the educational process is a request of the modern conditions.Due to the media resources, quality education can be achievable.
An Approach for Multi-Level Visibility Scoping of IoT Services in Enterprise Environments

In IoT, what services from which nearby devices are available, must be discovered by a user's device (e.g., smartphone) before she can issue commands to access them. Service visibility scoping in large scale, heterogeneous enterprise environments has multiple unique features, e.g., proximity based interactions, differentiated visibility according to device natures and user attributes, frequent user churns thus revocation. They render existing solutions completely insufficient. We propose Argus, a distributed algorithm offering three-level, fine-grained visibility scoping in parallel: i) Level 1 public visibility where services are identically visible to everyone; ii) Level 2 differentiated visibility where service visibility depends on users’ non-sensitive attributes; iii) Level 3 covert visibility where service visibility depends on users’ sensitive attributes that are never explicitly disclosed. Extensive analysis and experiments show that: i) Argus is secure; ii) its Level 2 is 10x as scalable and computationally efficient as work using Attribute-based Encryption, Level 3 is 10x as efficient as work using Paring-based Cryptography; iii) it is fast and agile for satisfactory user experience, costing 0.25 s to discover 20 Level 1 devices, and 0.63 s for Level 2 or Level 3 devices.
Identity Preserve Transform: Understand What Activity Classification Models Have Learnt

Activity classification has observed great success recently. The performance on small dataset is almost saturated and people are moving towards larger datasets. What leads to the performance gain on the model and what the model has learnt? In this paper we propose identity preserve transform (IPT) to study this problem. IPT manipulates the nuisance factors (background, viewpoint, etc.) of the data while keeping those factors related to the task (human motion) unchanged. To our surprise, we found popular models are using highly correlated information (background, object) to achieve high classification accuracy, rather than using the essential information (human motion). This can explain why an activity classification model usually fails to generalize to datasets it is not trained on. We implement IPT in two forms, i.e. image-space transform and 3D transform, using synthetic images. The tool will be made open-source to help study model and dataset design.
The Role of Traditional Features in Authorship Attribution

As an important direction of natural language processing, authorship attribution has been paid much attention. Nowadays, the research methods mainly based on neural network and make great progress. However, compared with traditional methods, the interpretability of these methods has certain limitations. It is difficult for us to know which features are specifically used in the classification of neural network models, and the weight distribution of these features, etc. Without an exact understanding of the model, it is difficult for us to use it in key fields. We used 162 manually defined features at 5 levels and n-grams features at the character level to conduct authorship attribution experiment on the Chinese data set, and conducted a comparative study on these features to obtain the features that the model plays an important role in authorship attribution of Chinese text.
Mining Semantic Subspaces to Express Discipline-Specific Similarities

Word embeddings enable state-of-the-art NLP workflows in im-portant tasks including semantic similarity matching, NER, question answering, and document classification. Recently also the biomedical field started to use word embeddings to provide new access paths for a better understanding of pharmaceutical entities and their relationships, as well as to predict certain chemical properties. The central idea is to gain access to knowledge embedded, but not explicated in biomedical literature. However, a core challenge is the interpretability of the underly-ing embeddings model. Previous work has attempted to inter-pret the semantics of dimensions in word embeddings models to ease model interpretation when applied to semantic similarity task. To do so, the original embedding space is transformed to a sparse or a more condensed space, which then has to be inter-preted in an exploratory (and hence time-consuming) fashion. However, little has been done to assess in real-time whether specific user-provided semantics are actually reflected in the original embedding space. We solve this problem by extracting a semantic subspace from large embedding spaces that better fits the query semantics defined by a user. Our method builds on least-angle regression to rank dimensions according to given semantics properly, i.e. to uncover a subspace to ease both in-terpretation and exploration of the embedding space. We com-pare our methodology to querying the original space as well as to several other recent approaches and show that our method consistently outperforms all competitors.
Improved Logistic Map Based Algorithm for Biometric Image Encryption

Tumultuous maps are ordinarily favored for the age of arbitrary numbers in encryption calculations because of high arbitrariness, capriciousness, aperiodic, and affectability. These calculations are completely required upon the underlying qualities and bear high correspondence to cryptographic calculations. Right now, the proposed encryption calculations are dependent on improved strategic guide for biometric picture encryption. The proposed calculated guide is improved to conquer its lacuna concerning haphazardness, irregular dispersion, unbound, and little parameter space. Besides the proposed calculated guide produces arbitrary numbers dependent on ongoing factors, haphazardly chose qualities and the last numbers are diffused arbitrarily through XOR activity. Trial results on different biometric unique mark pictures exhibit better encryption parameters and around level histogram of the encoded pictures that expand trouble during unapproved unscrambling. The proposed calculation can be viably considered for multi-biometric unique mark picture encryption in government and nongovernment associations because of its less intricacy and less encryption time.