Reviewerly/exHarmony huggingface dataset:
Analysing Information Security Risk Ontologies

This research work presents existing security ontologies and identifies relevant security ontology requirements in information systems. Moreover, it proposes a new classification of security ontologies in which, two main families, namely ontologies-based security standards and ontologies-based security risk assessment, are defined. For each family, a set of related research works is selected and a thorough description of their security ontologies is presented. The purpose of this analysis is to identify security ontology requirements as well as ontological characteristics for each study in order to help a security decision maker to select an ontology based off of their security risks and requirements as well as their needed security models and standards. By selecting the appropriate ontology, security stakeholders support security compliance and risk assessment in an enterprise.
Towards Automatic Evaluation of Reused Answers in Community Question Answering

We consider the problem of reused answer retrieval for community question answering (CQA): given a question q, retrieve answers $$a_{i}^{j}$$ posted in response to other questions $$q_{i} (\ne q)$$, where $$a_{i}^{j}$$ serves as an answer to q. While previous work evaluated this task by manually annotating the relationship between q and $$a_{i}^{j}$$, this approach does not scale for large-scale CQA sites. We therefore explore an automatic evaluation method for reused answer retrieval, which computes nDCG by defining the gain value of each retrieved answer as a ROUGE score that treats the original answers to q as gold summaries. Our answer retrieval experiment suggests that effective reused answer retrieval systems may not be the same as effective gold answer retrieval systems. We provide case studies to discuss the benefits and limitations of our approach.
A QoS&amp;SLA-Driven Multifaceted Trust Model for Cloud Computing

Quality of Service (QoS) plays a vital role in cloud computing while Service Level Agreements (SLA) to a service contract is indispensable as well. Selecting a trusted cloud service based on service performance, thus, is raising fundamental concern. This work presents a QoS&SLA-driven multifaceted trust model for efficiently evaluating the trustworthiness of a cloud service in the light of its multiple differential service attributes. Owing to the uncertainty of QoS, the interval number theory is naturally introduced into our trust model. In the trust evaluation, moreover, an adaptive weight adjustment method that depends on connection number is exploited to dynamically accommodate their respective factors. The proposed trust model is the composition of two types of trust metrics, which are QoS trust and user satisfaction trust. QoS trust, specifically, that indicates the level of actual performance of the cloud service. User satisfaction trust virtually reflects to what extent actual service performance is in accord with SLA. Finally, we assess the proposed trust model based on real datasets derived from CloudHarmony, which makes the approach more objective and effective for cloud computing.
Rule-Based Approach to Sentence Simplification of Factual Articles

A model for automatic text simplification by splitting the longer sentences from a given text has been presented. The paper’s proof-of-concept model demonstrated the following capabilities: (i) the average number of characters per sentence in the output text is reduced to 54% of that of the input text, (ii) 90% of the newly generated sentences are comparable with those generated by a human expert. The Flesch-Kincaid Grade Level (FKGL) of text complexity used in this experiment is 16.5, suitable for graduate students. The FKGL of the processed text is 9.7, suited for tenth grade students. Sentence simplification has various applications in natural language processing, including automatic question generating systems. This work is aimed at simplification so as to enhance accessibility of text and its comprehension by learning disabled children.
A Research on Real-Name Blockchain System Bind Health Passbook Electronic Medical Record Exchanges Mechanism

With the gradual development of the global information technology and mobile communication networks, medical system with information and network technologies has become an inevitable trend. All countries are also accelerating their efforts to replace traditional paper books with Electronic Medical Record (EMR). Medical records and electronic medical record implementation can save costs annually, increase economic efficiency by improving the efficiency and safety of medical systems, and accelerate innovation in the medical industry. The implementation of EHR will undoubtedly bring many conveniences, stakeholders who are concerned with EHR at this stage are still not satisfied with the privacy disclosure and information security regarding the possible implementation of electronic medical records. And the information between them still lacks good interoperability. Designing a personal health passbook exchange mechanism with high safety strength specifications to protect the special funds, two-factor identity card technology with smart cards, an authorization mechanism for applying real-name block chain system and digital signature to medical records, and the overall system integrate the existing electronic medical record signing mechanism with the overall personal health passbook exchange mechanism to identify users from the very beginning as empowered to grant users the right to personal health passbook exchange. According to the development vision of the Ministry of Health and Welfare, electronic medical records are stored through mobile carriers (health passbooks) to achieve a win-win situation of promoting universal health and medical progress from an information security point of view.
Rare Geometries: Revealing Rare Categories via Dimension-Driven Statistics

In many situations, classes of data points of primary interest also happen to be those that are least numerous. A well-known example is detection of fraudulent transactions among the collection of all financial transactions, the vast majority of which are legitimate. These types of problems fall under the label of 'rare-category detection.' There are two challenging aspects of these problems. The first is a general lack of labeled examples of the rare class and the second is the potential non-separability of the rare class from the majority (in terms of available features). Statistics related to the geometry of the rare class (such as its intrinsic dimension) can be significantly different from those for the majority class, reflecting the different dynamics driving variation in the different classes. In this paper we present a new supervised learning algorithm that uses a dimension-driven statistic, called the kappa-profile, to determine whether unlabeled points belong to a rare class. Our algorithm requires very few labeled examples and is invariant with respect to translation so that it performs equivalently on both separable and non-separable classes.
Breast Abnormality Detection Using LBP Variants and Discrete Wavelet Transform

The second largest disease leading to the death of women is Breast Cancer. To decrease mortality rate in women breast cancer should be diagnosed early. Digital mammography screening plays vital role for early diagnosis of breast cancer. This work presents a method for two types of classification of breast tissues in mammograms. First type of classification uses texture descriptors like Local Binary Pattern (LBP) and its variants such as Local Quinary Pattern (LQP) and Local Ternary Pattern (LTP) to extract the features for the classification of mammograms into normal-abnormal. Second type of classification employs discrete wavelet transform and LBP variants to classify breast tissues into benign-malignant. SVM classifier is used for the validation of our methods. The work was carried out by considering 48 ROIs (Region of Interest) or breast tissues segmented from mammograms. We achieved 100% accuracy in classifying Region of interests into normal and abnormal using LBP and its variants. The highest accuracy of 82.19% is achieved in classifying abnormal Region of Interests into benign and malignant using discrete wavelet transform and LBP variants.
Enhancing the Adaptiveness of Gaussian Process Regression based on Power Spectral Density

In many years, Gaussian process was popularly utilized in many research areas such as signal processing, data communications and image processing, etc. Unlike other techniques which try to determine all of the parameters of system model, Gaussian process adapts these parameters to reflect the actual underlying model. Because of that, this approach can be explicitly addressed as a non-parametric methodology. As a comparison to other well-known methods, Gaussian process regression (GPR) possesses much better performance in terms of precision and versatility. However, this technique does have some drawbacks. One of them is the adaptiveness to the complex data. In this research, we would like to introduce a novel solution based on power spectral density to adapt the model for better accuracy.
Query Recommendation to Draw a Laugh from Web Searchers

This study proposes a system, which shows funny term pairs when searchers issue a query into Web search engines. The proposed system analyzes the following two factors for a term pair in a given query: the unexpectedness and the semantic conflict. The experimental result showed that the proposed method provided a larger number of funny term pairs for queries than the baseline methods. Although the proposed method was not the best based on the average value, it can still offer opportunities for searchers to laugh and feel cheery when issuing queries into the Web search engines.
Conditional Status Detection for Factory Management System with Optimized Predictive Modeling

The idea is to build a wireless factory management system which can perform surveillance of the premises and report abnormal conditions by sensing parametric deviations in temperature, pressure, humidity, wind speed, and visibility using microcontrollers and sensor-based remote terminal unit (RTU). In this paper, two constraint functions were constructed which evaluate the conditional status of the site in terms of abnormality and help in the classification of the variables. Various machine learning algorithms like logistic regression, neural network, naive Bayes, random forest and gradient boosting with hyperparameter tuning are used, which predict the conditional outcome with a 95% accuracy. Also, the constraint functions are compared by various statistically and graphically parameters such as KS statistic, ROC curve and AUC, gain, lift and actual versus predicted charts. The results indicate an optimization of 2% in the accuracy of the predictive model by using the weighted percentage method.
Sketch Based Image Retrieval with Adversarial Network

Sketch retrieval is a specific cross-domain retrieval task. The core of sketch retrieval is to learn a common feature subspace, where the features of sketches and natural images can be both discriminative and domain-invariant. However, similarity constraints can impair the performance of the feature extractor, resulting in unsatisfactory retrieval accuracy. For this problem, we propose a novel sketch based image retrieval method based on adversarial network. Our method is demonstrated as follows: Firstly, we train the sketch image network and natural image network to improve the ability of classification; secondly, we train the adversarial network to promote the feature fusion of sketches, of which the network is constituted by feature extractor and domain classifier; thirdly, we use the deep convolutional neural network to extract the deep feature to achieve retrieval. Experiments on retrieval show positive results.
Enhanced Decision Tree Algorithm for Discovery of Exceptions

Decision trees are the most admired and extensively used classification algorithms in data mining. These are considered accurate, easy to use, and comprehensible classifiers. Like many other classification models, decision tree classifiers ignore exceptions as noise. Exceptions are precious pieces of knowledge that are required to modify our decisions in extraordinarily rare circumstances. Since exceptions pertain to a tiny number of instances, it is a very challenging task to capture exceptions since a learning algorithm’s focus is on discovering knowledge with high generalization power. This paper proposes an enhanced decision tree learning algorithm that accommodates exceptions. The working of the suggested algorithm is demonstrated on a toy example dataset. It is further applied to three datasets obtained from machine learning repository. The results reveal that the Enhanced Decision Tree Algorithm (EDTA) discovers several exceptions across the experimental datasets.
Improving Arabic Microblog Retrieval with Distributed Representations

Query expansion (QE) using pseudo relevance feedback (PRF) is one of the approaches that has been shown to be effective for improving microblog retrieval. In this paper, we investigate the performance of three different embedding-based methods on Arabic microblog retrieval: Embedding-based QE, Embedding-based PRF, and PRF incorporated with embedding-based reranking. Our experimental results over three variants of EveTAR test collection showed a consistent improvement of the reranking method over the traditional PRF baseline using both MAP and P@10 evaluation measures. The improvement is statistically-significant in some cases. However, while the embedding-based QE fails to improve over the traditional PRF, the embedding-based PRF successfully outperforms the baseline in several cases, with a statistically-significant improvement using MAP measure over two variants of the test collection.
Commercial secrets as an information with restricted access: analysis of legislative practice

The article deals with the analysis of the “commercial secret” concept and the peculiarities of the regulation of public relations in this field, as well as the justification of the classification of this concept to information with restricted access (namely classified information). The author provided a generalized notion of classified information and proposed to unify the legislative definition of the term “commercial secret”. Some proposals have been made to amend the legislative regulation of information public relations in Ukraine.
Building a Web-Based Federated Toolchain

Big companies use many tools, jointly referred to as the toolchain, to manage vast amounts of engineering data being generated across an application lifecycle. Individual tools are typically designed to perform specific engineering tasks, and rely on specific data formats. This leads to problems when attempting to automate engineering tasks that are not supported by a particular tool, and which require data from multiple tools. This paper presents the experiences and lessons learned from an industrial research-project within the heavy vehicle manufacturer Scania, where the project goal was to identify and industrialize technologies and principles that solve the above problem. The presented lessons cover architectural, technological, and organizational aspects of a toolchain development-process. In addition, as a consequence of the lessons learned, the toolchain architecture and tool-interface architecture is also presented.
Two Finger Gesture Imitating Animatronic Hand

Abstract This paper poses the discussion regarding animatronics that has been aided with modern trends in technological innovations. The ease of availability and procurement of present day items incorporate economical factors into research. To accomplish the aforementioned, a simple two fingered hand model has been developed that can be remotely controlled using a glove. The glove can be manipulated via a hand. It has been electronically modulated using Arduino Uno and servo motors. The discussion has been furthered to the amalgamation of future developments that can pave the way for commercialization of such devices. Hardships and challenges faced during building of the model and experimentation of the finalized model are detailed.
Does Semantic Search Performs Better than Lexical Search in the Task of Assisting Legal Opinion Writing?

Many of the criminal cases analysed by the Prosecution Office of the Federal District and Territories are repetitive and processing them can be streamlined by providing similar previous cases as template. We investigate the use of information retrieval techniques to enable automated identification of similar cases and evaluate if semantic search performs better than lexical search in the task of assisting legal opinion writing. As a proof of concept, syntactic indexing (TF-IDF and BM25) and semantic indexing (Latent Semantic Indexing - LSI and Latent Dirichlet Allocation - LDA) techniques were evaluated using document collections from two public prosecutors offices. In addition, we evaluate model enrichment with the use of recorded data about the cases, and also with the legal norm citations observed in documents. Baseline document collections sampled from full document collection from two public prosecutors offices were used for model evaluation utilizing Normalized Discounted Cumulated Gain (NDCG) as metric. We conclude that there is no significant performance difference between semantic and syntactic indexing techniques. In addition, we observe no significant performance gain with model enrichment. We chose the BM25 technique as more adequate because it has a good balance between performance and simplicity.
Chosen base‐point side‐channel attack on Montgomery ladder with <i>x</i> ‐only coordinate: with application to secp256k1

This study revisits the side-channel security of the elliptic curve cryptography (ECC) scalar multiplication implemented with Montgomery ladder. Focusing on a specific implementation that does not use the y -coordinate for point addition (ECADD) and point doubling (ECDBL), the authors show that Montgomery ladder on Weierstrass curves is vulnerable to a chosen base-point attack. Unlike the normal implementation with y -coordinate, in the scenario of this study, the chosen base-point strategy will not lead to operations with two same inputs during the ECADD and/or ECDBL. Instead, by choosing a suitable base-point, one will find that there are operations that share a common operand; while it is not the case if the base-point is not chosen correctly. This results in the recovery of the secret (fixed) scalar. They also experiment the methods of shared operand detection on a real-world SoC, where a secp256k1 dedicated Montgomery ladder scalar multiplication with x -only coordinate is implemented, to show the efficiency of the scalar recovery attack. Naturally, the attack can be generalised to other Weierstrass curves when they contain special points.
Automatic bounding-box-labeling method of occluded objects in virtual image data

Computer vision technology is widely used based on its massive and correct data set, of which the bounding box labeling is a common method. Aimed at a large number of original image data set produced by virtual simulation, we proposed an automatic pixel-level bounding-box-labeling method to solve problem of accuracy and speed. The method starts by a fundamental algorithm based on targeted bounding box, which will be adopted to label the images produced by virtual simulation and learn from the bounding box of different objects; Next, the method will find consistent seed points and apply region growing algorithm to automatically produce binary images based on the seed points; Then, an occlusion-estimating algorithm can be used to evaluate the occluded conditions in the binary image; Finally, employ bounding-box-labeling algorithm to label targeted objects according to various occlusion. Apply the data set from 2019 Small Target Competition held by China Society of Images and Graphics to test and verify our method, the result turns out that this method can solve the occlusion problem especially the truncate occlusion and can label the objects' entire body precisely.
About Vertex Mappings

Summary In [6] partial graph mappings were formalized in the Mizar system [3]. Such mappings map some vertices and edges of a graph to another while preserving adjacency. While this general approach is appropriate for the general form of (multidi)graphs as introduced in [7], a more specialized version for graphs without parallel edges seems convenient. As such, partial vertex mappings preserving adjacency between the mapped verticed are formalized here.
Detecting Dependency Between Discrete Random Variables and Application

Finding the relationship between two random variables is important because it brings insights to design appropriate probabilistic models for a given dataset. The problem with current measures like Maximal Information Coefficient (MIC) is that can take a considerable amount of time to compute it, thus faster methods are desirable. In this paper, we propose two novel measures capable to detect linear and nonlinear dependency between random variables. The proposed measures are used to design new correlation functions, in order to compare them with the ones in literature. These novel dependency measures have been applied to supervised feature selection to show their usefulness.
Measurement in Software Engineering

Measurement in software is a basic process in all parts of the software development life cycle because it helps to express the quality of a software. But in software engineering, measurement is difficult and not precise. However, researchers accept that any measure is better than zero measure. In this chapter, the software metrics are explained, and some software testing tools are introduced. The software metric sets of Chidamber and Kemerer Metric Set (CK Metric Set), MOOD Metric Set (Brito e Abreu Metric Set), QMOOD Metric Set (Bansiya and Davis Software Metric Set), Rosenberg and Hyatt Metric Set, Lorenz and Kidd Metric Set (L&amp;K Metric Set) are explained. The software testing tools such as Understand, Sonargraph, Findbugs, Metrics, PMD, Coverlipse, Checkstyle, SDMetrics, and Coverity are introduced. Also, 17 literature studies are summarized.
Implementing a Domain-Independent Framework to Detect Suspicious Review Patterns

Nowadays detecting opinion spam has attracted a lot of attention. Different approaches have been taken to tackle this problem. However, most of these approaches are neither domain-independent nor scalable. In this paper, we focus on the similar but more general problem on how to detect abnormal patterns in reviews. These abnormal patterns can indicate potential spam activities. We will implement a scalable, domain-independent framework with the help of the Hadoop ecosystem tools. We will extract certain relationships between different fields in our data and the rating that a review gave a product or service, and in turn determine how “abnormal” these relationships are based on statistics acquired from the entire dataset. We applied this technique to three datasets in different domains and found such abnormal patterns, which indicate potential spammers and/or spam activities.