Reviewerly/exHarmony huggingface dataset:
Unstructured Text Resource Access Control Attribute Mining Technology Based on Convolutional Neural Network

In the attribute-based access control (ABAC) model, attributes are the basis for controlling access to data resources. The existing attribute extraction methods that are based on manual management are time consuming and have a high cost, variable accuracy, and poor scalability when dealing with massive unstructured text from big data resources. This paper proposes a multidimensional hybrid feature generation method for text resource attributes. The method comprehensively calculates the characteristics of attributes themselves, the relationships between attributes, and the relationship between attributes and resources. It can fully and accurately characterize the attributes. It converts attribute features into grayscale images in order to translate attribute mining problems into image recognition problems. We propose an attribute mining method based on a convolutional neural network (CNN). We use neural networks to automatically correlate features. It means there is no need to manually consider the importance of features and their relationships. This avoids the need for security experts to manually label the attributes of massive resources and facilitates the automatic and intelligent mining of ABAC resource attributes. The experimental results show that compared with the benchmark algorithm, the proposed method has improved accuracy and recall rate and can provide attribute support for ABAC of big data resources.
Performance Influence of Security Function Chain Ordering

In modern days security systems often reach their performance peak and limit the protected application. Utilizing the available resources for security more efficiently is becoming more critical. In this paper, we introduce the claim, that no static security function chain is optimal in every situation. First experiments prove our claim.
Robot Localization in the Magnetic Unstable Environment

In the recent years we have developed a library called robo-guide, designed for our mobile robots to assist visitors with guidance in the building of our department. In the process of development we faced the problem of proper robot localization, what was caused by magnetic disturbances in the building. Using robots equipped with magnetic compass for navigating on the map, the proper estimation of position using, for example, histogram or particle filtering is substantially difficult, because expected readings from radar can be shifted. To overcome this problem we used the map of magnetic anomalies and the epsilon granules to estimate proper north direction, which let us to minimize the error between expected and actual direction. We performed real life experiments with our robots. The results of the experiments show satisfying effectiveness of our approach.
Constructing weak simulations from linear implications for processes with private names

Abstract This paper clarifies that linear implication defines a branching-time preorder, preserved in all contexts, when used to compare embeddings of process in non-commutative logic. The logic considered is a first-order extension of the proof system BV featuring a de Morgan dual pair of nominal quantifiers, called BV1. An embedding of π -calculus processes as formulae in BV1 is defined, and the soundness of linear implication in BV1 with respect to a notion of weak simulation in the π -calculus is established. A novel contribution of this work is that we generalise the notion of a ‘left proof’ to a class of formulae sufficiently large to compare embeddings of processes, from which simulating execution steps are extracted. We illustrate the expressive power of BV1 by demonstrating that results extend to the internal π -calculus, where privacy of inputs is guaranteed. We also remark that linear implication is strictly finer than any interleaving preorder.
Is It All About Frequency?

Being digitally competent requires appropriate knowledge, skills and attitudes towards the use of digital technologies for different aspects of today’s society. The role Education plays in equipping students with such competence is increasingly important, but despite the growing number of studies on the topic, little is known about the impact the use of digital technologies has on students’ digital competence. This paper presents and discusses findings regarding the impact of tablet use on the development of lower secondary students’ digital competence. A retrospective evaluation was made resorting to a questionnaire applied to students from two schools. Findings suggest that the frequency of tablet use seems to explain the development of digital competence as well as students’ perceptions of their digital competence proficiency level.
Information Retrieval Models for Contact Recommendation in Social Networks

The fast growth and development of online social networks has posed new challenges for information retrieval and, as a particular case, recommender systems. A particularly compelling problem in this context is recommending network edges, that is, automatically predicting people that a given user may wish or benefit from connecting to in the network. This task has interesting particularities compared to more traditional recommendation domains, a salient one being that recommended items belong to the same space as the users they are recommended to. In this paper, we investigate the connection between the contact recommendation and the text retrieval tasks. Specifically, we research the adaptation of IR models for recommending contacts in social networks. We report experiments over data downloaded from Twitter where we observe that IR models, particularly BM25, are competitive compared to state-of-the art contact recommendation methods. We further find that IR models have additional advantages in computational efficiency, and allow for fast incremental updates of recommendations as the network grows.
Breast Cancer Classification: A Deep Learning Approach for Digital Pathology

Breast cancer is the second leading cause of cancer death among women. Breast cancer is not a single disease, but rather is comprised of many different biological entities with distinct pathological features and clinical implications. Pathologists face a substantial increase in workload and complexity of digital pathology in cancer diagnosis due to the advent of personalized medicine, and diagnostic protocols have to focus equally on efficiency and accuracy. Computerized image processing technology has been shown to improve efficiency, accuracy and consistency in histopathology evaluations, and can provide decision support to ensure diagnostic consistency. We propose using deep learning and convolutional neural networks (CNN) to classify a subset of breast cancer histopathological images of benign and malignant breast tumors, from the publicly available BreakHis dataset. We design a workflow featuring patch extraction from whole slide images, CNN training and performance evaluation to solve this problem.
Low-Level Greyscale Image Descriptors Applied for Intelligent and Contextual Approaches

The process of image recognition and understanding is not always a trivial task. The automatic analysis of the image content can be difficult and not obvious. Usually, it requires the identification of particular objects visible in a scene, however, this assumption not always provides the expected results. In many cases, the whole context of an image or relations between objects provide important information about an image and can lead to other conclusions than in case of the analysis of single objects separately. Hence, the obtained result can be considered more 'intelligent'. The contextual analysis of images can be based on various features. Amongst them the low-level descriptors are successfully applied in the problem of image analysis and recognition. Using the obtained representations of objects one can conclude the context of an image as a whole. In the paper the possibility of applying selected greyscale descriptors in the intelligent systems is analytically and experimentally analyzed. The works have been performed by means of algorithms employing the transformation of pixels from Cartesian into polar co-ordinates.
NLME: a nonlinear motion estimation-based compression method for animated mesh sequence

This paper proposes an efficient compression algorithm for animated three-dimensional meshes by introducing nonlinear transformations to model the motion field of deforming patches. First, a segmentation process is applied to separate the 3D model into different patches which have similar motion patterns through the sequence. Next, the motion of the resulting patches is accurately described by a nonlinear motion estimation model. The main idea is to exploit the temporal coherence of the geometry component by using a nonlinear predictor in order to get better approximation of vertex locations. Nonlinear motion transforms are computed at previous frame to match the subsequent ones. Moreover, an adaptive bit allocation algorithm is employed to determine the near-optimal number of bits for quantizing the prediction errors. The number of quantization bits for each segmented patch is determined by analyzing the geometry complexity of the patch and the statistical properties of the prediction errors. Finally, an extensive experimental study has been conducted to evaluate the coding efficiency of the proposed compression scheme. Simulation results demonstrate that the proposed method is very efficient in terms of rate-distortion performance, particularly for the animated models with non-rigid deformations, and outperforms the state-of-the-art methods.
Improving parallel efficiency for asynchronous graph analytics using Gauss‐Seidel‐based matrix computation

Summary Graph analytics is extensively used in big‐data applications such as social networks, web analysis, bio‐informatics, etc. Most graph processing frameworks adopt vertex‐centric model due to its ease of use and programming. However, when dealing with asynchronous graph analytics, frameworks based on vertex programming perform inefficiently. The reason is that first, vertex programming must guarantee the sequential consistency, which means frequent use of locks or atomic operations, and second, the algorithms are parallelized in vertex level and latent parallelism of the algorithms cannot be exploited. To improve parallel efficiency of asynchronous graph processing, the Gauss‐Seidel style algorithms in particular, this paper proposes a scheduling model using Gauss‐Seidel‐based matrix computation, which converts the vertex programming into two main matrix operations and then algorithms are parallelized by row and column vectors. Compared to vertex programming, our model parallelizes algorithms in a finer way to exploit more latent parallelism, while retains the ease‐of‐programming advantage of vertex programming. Instead of using locks to guarantee the sequential consistency, our model uses a hybrid synchronization policy to reduce serializability among threads and overheads of context switching. Furthermore, this model strengthens locality of the program. Experiment results show that our model outperforms vertex‐centric asynchronous frameworks in both performance and scalability. Moreover, it even surpasses the matrix‐based synchronous framework GraphMat with some non‐Gauss‐Seidel style algorithms.
Rethinking the University of Maryland Authority File for the Linked Data Environment

The development and implementation of a new digital collections system built on the Linked Data Platform has provided University of Maryland Libraries with an ideal opportunity to prototype and test ways to model local corporate name authorities in resource description framework (RDF). This includes assessing the local corporate names metadata, reconciling these names against existing authorities, and devising and executing an RDF model for unreconciled names in support of the new linked data environment.
Reliability-Aware and Robust Multi-sensor Fusion Toward Ego-Lane Estimation Using Artificial Neural Networks

In the field of road estimation, incorporating multiple sensors is essential to achieve a robust performance. However, the reliability of each sensor changes due to environmental conditions. Thus, we propose a reliability-aware fusion concept, which takes into account the sensor reliabilities. By that, the reliabilities are estimated explicitly or implicitly by classification algorithms, which are trained with extracted information from the sensors and their past performance compared to ground truth data. During the fusion, these estimated reliabilities are then exploited to avoid the impact of unreliable sensors. In order to prove our concept, we apply our fusion approach to a redundant sensor setup for intelligent vehicles containing three-camera systems, several lidars, and radar sensors. Since artificial neural networks (ANN) have produced great results for many applications, we explore two ways of incorporating them into our fusion concept. On the one hand, we use ANN as classifiers to explicitly estimate the sensors’ reliabilities. On the other hand, we utilize ANN to directly predict the ego-lane from sensor information, where the reliabilities are implicitly learned. By the evaluation with real-world recording data, the direct ANN approach leads to satisfactory road estimation.
Optical Character Recognition from Printed Text Images

In recent years, text extraction from document images is one of the most widely studied topics in Image Analysis and Optical Character Recognition. These extractions of document images can be used for document analysis, content analysis, document retrieval and many more. Many complex text extracting processes Maximization Likelihood (ML), Edge point detection, Corner point detection etc. are used to extract text documents from images. In this article, the corner point approach was used. To extract document from images we used a very simple approach based on FAST algorithm. Firstly, we divided the image into blocks and their density in each block was checked. The denser blocks were labeled as text blocks and the less dense were the image region or noise. Then we check the connectivity of the blocks to group the blocks so that the text part can be isolated from the image. This method is very fast and versatile, it can be used to detect various languages, handwriting and even images with a lot of noise and blur. Even though it is a very simple program the precision of this method is closer or higher than 90%. In conclusion, this method helps in more accurate and less complex detection of text from document images.
Object detection for panoramic images based on MS‐RPN structure in traffic road scenes

The objection detection of panoramic image is the key part of street view, intelligent transportation, automatic driving and other technologies. Due to the shortcomings of existing algorithms in detecting panoramic images, firstly a high‐resolution panoramic image dataset is introduced, then the multi‐scale feature pyramid networks (MS‐RPN) structure is proposed and a new network with Sim‐Inception module is designed. The network can extract different scales of objects from different feature layers, so that the small object in the image can also be accurately detected. Finally, the entire detection network is trained by using the dataset constructed in this study. Meanwhile, the ROIPool is replaced by ROIAlign and the loss function is adjusted according to the network structure. The experimental results show that the detection performance on the panoramic dataset is significantly improved by authors’ proposed algorithm, which is better than other deep learning algorithms, especially for small object in the image.
Automated Latin Text Detection in Document Images and Natural Scene Images based on Connected Component Analysis

Robust and accurate detection of text in natural scene images and document images is a very challenging and common research problem. Over the past few decades, a variety of algorithms for text detection in images have been developed but there is still need for more robust and accurate text detection methods. In this work, we have proposed an accurate and robust text detection framework in which canny edge detection, maximally stable extremal regions and geometric filtering are employed in combination to efficiently collect and filter letter candidates in an image. Subsequently, individual letter patches are grouped to detect text sequences, which are then fragmented into isolated word patches. Finally, optical character recognition is employed to digitize the word patches. The proposed algorithm is tested on images representing different scenarios ranging from documents to natural scenes. Promising results have been reported which prove the accuracy and robustness of the proposed framework and encourage its practical implementation in real world applications.
Secure pairing via video and IMU verification

Secure pairing is an important problem especially due to large number of IoT devices. In this paper, we propose PosePair++, to enable a camera to securely pair with IoT devices which are equipped with IMU sensors. Existing context-based pairing approaches do not adequately address this problem due to differing sensing modalities. To address this challenge, we propose to translate the signals from heterogeneous sensing modalities to a common space, namely 2D acceleration. In this demo, we present PosePair++'s robustness against different types of attackers (i.e., attackers that observe the user's motion, or attackers performing mimicking attack).
A Coordinated Search Strategy for Solitary Robots

Coordination without a priori information about the environment is an important problem in robotics, with applications from formation control to search and rescue. We propose a strategy based on cellular decomposition for coordinating search of solitary robots, with each robot considering cells assigned to others as soft obstacles. Simulations indicate the utility of the strategy when there is insufficient time to search the entire environment.
Is ArguMessage Effective? A Critical Evaluation of the Persuasive Message Generation System

This paper describes an investigation into the effectiveness of ArguMessage, a system that uses argumentation schemes and limited user input to semi-automatically generate persuasive messages encouraging behaviour change that follow specific argumentation patterns. We conducted user studies in the domains of healthy eating and email security to investigate its effectiveness. Our results show that ArguMessage in general supported users in generating messages based on the argumentation schemes. However, there were some issues in particular with copying the example messages, and some system improvements need to be made. Participants were generally satisfied with the messages produced, with the exception of those produced by two schemes (‘Argument from memory with goal’ and ‘Argument from values with goal’) which were removed after the first study.
Implementing Smart Virtual Product Development (SVPD) to Support Product Manufacturing

This paper illustrates the concept of providing the manufacturing knowledge during early stages of product life cycle to experts working on product development. The aim of this research is to enable a more collaborative product development environment by using Smart Virtual Product Development (SVPD) system, which is powered by Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA). It enhances the industrial product development process by storing, using and sharing previous manufacturing experience and knowledge. This knowledge is stored in form of formal decisional events after being collected from the set of similar products having some common functions and features. The proposed system uses a collective, team-like knowledge developed by product designers, manufactures, and metrologists. Implementing this system in the process of product development enables the small and medium enterprises (SMEs) to take proper decisions at appropriate time by reducing mistakes at an early stages of product development.
Design and Analysis of Switch Median Filters for Salt and Pepper Noise

An algorithm based on signal local difference and an algorithm based on signal directional difference for detecting salt and pepper noise are designed in this article. Both of them belong to two-stage noise detection algorithms, and the first-stage detection for both is based on gray-scale range criterion. The difference between the two algorithms embodies in the second-stage detection. The former is based on local difference criterion, and the later is based on directional difference criterion. In the section of algorithm evaluation, first, the optimal parameter settings for the two algorithms are determined through analysis and experiments. Then, the denoising effects of the two algorithms are evaluated by denoising the test images with different noise density. The evaluation results show that the algorithm based on directional difference has better performance than the algorithm based on local difference. For both of the two algorithms, the denoising effects are inversely proportional to noise density. But more importantly, the two algorithms may be prone to misjudge the image-edge pixels or detail pixels as noise points. This will be the main improvement direction of the switch median filtering algorithm in the future.
Extracting Temporal Event Relations Based on Event Networks

Temporal event relations specify how different events expressed within the context of a textual passage relate to each other in terms of time sequence. There have already been impactful work in the area of temporal event relation extraction; however, they are mostly supervised methods that rely on sentence-level textual, syntactic and grammatical structure patterns to identify temporal relations. In this paper, we present an unsupervised method that operates at the document level. More specifically, we benefit from existing Open IE systems to generate a set of triple relations that are then used to build an event network. The event network is bootstrapped by labeling the temporal disposition of events that are directly linked to each other. We then systematically traverse the event network to identify the temporal relations between indirectly connected events. We perform experiments based on the widely adopted TempEval-3 corpus and compare our work with several strong baselines. We show that our unsupervised method is able to show better performance in terms of precision and f-measure over it supervised counterparts.
A Chaotic Cryptographic Solution for Low-Range Wireless Communications in Industry 4.0

Industry 4.0 refers the use of Cyber-Physical Systems as main technological component in industrial systems, but also in home and personal applications. Industrial systems tend to be critical, so privacy, trust and security are essential in this new revolutionary era. However, up to this moment, these deployments are most considered unsecure, especially when local-range communication technologies are employed. Very common systems such as the remote controls opening private areas in manufacturing companies are totally unprotected. Although this problem could be not prioritary in traditional production systems, in Industry 4.0 is critical, because of the great interdependency among components and the need of protecting the critical core managing the entire production system of a company. Therefore, in this paper it is proposed a lightweight cryptographic solution to protect low-range wireless communications. The proposed solution is based on chaotic masking technologies, which may be easily implemented in resource constraint embedded devices (as those employed in Industry 4.0). A real implementation of an infrared device including our solution is also described. An experimental validation is performed using the implemented real device.