Reviewerly/exHarmony huggingface dataset:
Are we better off with just one ontology on the Web?

Ontologies have been used on the Web to enable semantic interoperability between parties that publish information independently of each other. They have also played an important role in the emergence of Linked Data. However, many ontologies on the Web do not see much use beyond their initial deploy ment and purpose in one dataset and therefore should rather be called what they are – (local) schemas, which per se do not provide any interoperable semantics. Only few ontologies are truly used as a shared conceptualization between different parties, mostly in controlled environments such as the BioPortal. In this paper, we discuss open challenges relating to true re-use of ontologies on the Web and raise the question: "are we better off with just one ontology on the Web?"
Image tampering detection by estimating interpolation patterns

Several works have addressed the problem of detecting manipulations in images acquired from devices that use colour filter arrays, typical in the market due to low production costs. These devices use chromatic interpolation algorithms during the image formation process, allowing them to perform statistical analyses of inconsistencies generated from this process for authentication purposes. Most of the works focus on analysing the green band of the Bayer filter since it contains more information than blue and red bands. The lack of methods for effectively analysing other bands or different colour filters reduces the detection capability of known tools. The main purpose of this work is to provide a general methodology for detecting manipulations in this type of devices, in addition to providing new techniques that allow generalising the analysis in a great diversity of sensors.
Image Encryption Algorithm Based on a New Five Dimensional Hyperchaotic System and Sudoku Matrix

This paper advancement a new and active algorithm for the chaotic scheme centered on the Sudoku matrix, where a new five-dimensional chaotic scheme is created to create the random key into encrypting the color picture. The encryption system includes a scrambled image by using the chaotic sequence generated, then perform an XOR operation between the Sudoku Matrix and scramble, and then perform the scrambling process again, and then perform XOR between a chaotic key and the original image after previous processes. The power of the encrypting has been specified by analyzing the histogram, the correlation coefficient, Number of Pixels Change Rate (NPCR), the entropy, Unified Average Changing Intensity (UACI), and Peak Signal into Noise Ratio. Results showed that the characteristics of the proposed system are highly resistant to the various attacks. The time of encryption and decoding is very small.
Batch Alias Analysis

Many program-analysis based tools require precise points-to/alias information only for some program variables. To meet this requirement efficiently, there have been many works on demand-driven analyses that perform only the work necessary to compute the points-to or alias information on the requested variables (queries). However, these demand-driven analyses can be very expensive when applied on large systems where the number of queries can be significant. Such a blow-up in analysis time is unacceptable in cases where scalability with real-time constraints is crucial; for example, when program analysis tools are plugged into an IDE (Integrated Development Environment). In this paper, we propose schemes to improve the scalability of demand-driven analyses without compromising on precision. Our work is based on novel ideas for eliminating irrelevant and redundant data-flow paths for the given queries. We introduce the idea of batch analysis, which can answer multiple given queries in batch mode. Batch analysis suits the environments with strict time constraints, where the queries come in batch. We present a batch alias analysis framework that can be used to speed up given demand-driven alias analysis. To show the effectiveness of this framework, we use two demand-driven alias analyses (1) the existing best performing demand-driven alias analysis tool for race-detection clients and (2) an optimized version thereof that avoids irrelevant computation. Our evaluations on a simulated data-race client, and on a recent program-understanding tool, show that batch analysis leads to significant performance gains, along with minor gains in precision.
An Online Buffer-Aware Resource Allocation Algorithm for Multiuser Mobile Video Streaming

Mobile video traffic has experienced exponential growth in recent years due to increasing prevalence of mobile devices and continuous advancement of wireless communications. However, network fluctuations often occur when transmitting video data, which greatly impact the quality of experience (QoE) of mobile users. Buffering technique has been widely adopted to mitigate the effect of network fluctuations on QoE for mobile users by prefetching certain video data to mobile devices in advance. However, mobile users often abort video watching before the videos are fully watched and such early departure behavior can result in considerable wastage of buffered video data. In this paper, we study buffer-aware resource allocation for multiuser mobile video streaming system by jointly considering video users early departure behavior and wireless channel fluctuation property. We formulate a stochastic optimization problem in which the objective is to reduce both the amount of wasted buffered data and the video re-buffering time. The problem is then transformed into a series of deterministic per-slot problems by using Lyapunov optimization theory. We propose an online buffer-aware resource allocation algorithm to solve the per-slot optimization problems. After proving the convexity of the per-slot optimization problem, we further design a fast-convergent iterative algorithm based on Alternating Direction Method of Multipliers (ADMM) to perform each of the per-slot resource allocations. Simulation results show that our proposed algorithm has very low running time, near optimal performance, and is able to achieve a good trade-off between QoE improvement and data wastage reduction.
Image processing technology for text recognition

Image recognition and optical character recognition technologies have become an integral part of our everyday life due in part to the ever-increasing power of computing and the ubiquity of scanning devices. Printed documents can be quickly converted into digital text files through optical character recognition and then be edited by the user. Consequently, minimal time is required to digitize documents; this is particularly helpful when archiving volumes of printed materials. This study demonstrates how image-processing technologies can be used in combination with optical character recognition to improve recognition accuracy and to improve the efficiency of extracting text from images. Two software systems are developed and tested during this study: a character recognition system applied to cosmetic-related advertising images and a text detection and recognition system for natural scenes. The results of the experiment demonstrate that the proposed systems can accurately recognize text in images.
<i>XLearn</i>

Sensor-driven systems often need to map sensed data into meaningfully labelled activities to classify the phenomena being observed. A motivating and challenging example comes from human activity recognition in which smart home and other datasets are used to classify human activities to support applications such as ambient assisted living, health monitoring, and behavioural intervention. Building a robust and meaningful classifier needs annotated ground truth, labelled with what activities are actually being observed—and acquiring high-quality, detailed, continuous annotations remains a challenging, time-consuming, and error-prone task, despite considerable attention in the literature. In this article, we use knowledge-driven ensemble learning to develop a technique that can combine classifiers built from individually labelled datasets, even when the labels are sparse and heterogeneous. The technique both relieves individual users of the burden of annotation and allows activities to be learned individually and then transferred to a general classifier. We evaluate our approach using four third-party, real-world smart home datasets and show that it enhances activity recognition accuracies even when given only a very small amount of training data.
The topic of terrorism on Yahoo! Answers: questions, answers and users’ anonymity

Purpose The purpose of this paper is to explore the use of community question answering sites (CQAs) on the topic of terrorism. Three research questions are investigated: what are the dominant themes reflected in terrorism-related questions? How do answer characteristics vary with question themes? How does users’ anonymity relate to question themes and answer characteristics? Design/methodology/approach Data include 300 questions that attracted 2,194 answers on the community question answering Yahoo! Answers. Content analysis was employed. Findings The questions reflected the community’s information needs ranging from the life of extremists to counter-terrorism policies. Answers were laden with negative emotions reflecting hate speech and Islamophobia, making claims that were rarely verifiable. Users who posted sensitive content generally remained anonymous. Practical implications This paper raises awareness of how CQAs are used to exchange information about sensitive topics such as terrorism. It calls for governments and law enforcement agencies to collaborate with major social media companies to develop a process for cross-platform blacklisting of users and content, as well as identifying those who are vulnerable. Originality/value Theoretically, it contributes to the academic discourse on terrorism in CQAs by exploring the type of questions asked, and the sort of answers they attract. Methodologically, the paper serves to enrich the literature around terrorism and social media that has hitherto mostly drawn data from Facebook and Twitter.
Ontology-Assisted Enterprise Information Systems Integration in Manufacturing Supply Chain

Manufacturing communities around the globe are eagerly witnessing the recent developments in semantic web technology (SWT). This technology combines a set of new mechanisms with grounded knowledge representation techniques to address the needs of formal information modelling and reasoning for web-based services. This chapter provides a high-level summary of SWT to help better understand the impact that this technology will have on wider enterprise information architectures. In many cases it also reuses familiar concepts with a new twist. For example, “ontologies” for “data dictionaries” and “semantic models” for “data models.” This chapter presents the usefulness of a proposed architecture by applying a theory to integrating data from multiple heterogeneous sources which entails dealing with semantic mapping between source schema and a resource description framework (RDF) ontology described declaratively using specific query language (i.e. SPARQL) queries. Finally, the semantic of query rewriting is further discussed and a query rewriting algorithm is presented.
Cervical Cancer Detection and Classification from Pap Smear Images

In this paper, we propose a framework for detection and classification of cervical cancer from pap smear images. Early detection and accurate diagnosis of cervical cancer can reduce the death rate of cervical cancer patients. Pap smear or pap test is the most popular technique for early detection of cervical cancer. However, the manual analysis is labor intensive and time consuming process which relies on expert cytologist. Hence, it is needed to develop a computer aided diagnosis system to make the pap smear test more accurate and reliable. The objective of this paper is to present an innovative idea of applying random forest algorithm (RF) as a feature selection method using proposed bagging ensemble classifier for improving the predictive performance. The four basic steps of cervical cancer detection and classification system, image enhancement, segmentation, feature extraction and classification were used. K-means clustering combining with morphology operations obtained good segmentation for cell nuclei and cytoplasm. The most important features, shape, color and texture of nuclei and cytoplasm were applied to detect cervical cancer. To improve the accuracy of prediction results, random forest (RF) algorithm was used as a feature selection method. In classification stage, bagging ensemble classifier was applied which aggregated the results of five classifiers, linear discriminant (LD), support vector machine (SVM), weighted k-nearest neighbor (KNN), boosted trees and bagged trees. Herlev data set was used to prove the effectiveness of our proposed method. According to the experimental results, the high classification accuracy was achieved with top10 features using our proposed combined classifier. The accuracy was 97.83% in two class problem and 81.54% in seven class problem. When the results were compared with five classifiers, our proposed method was significantly better in two class and seven class problems.
Automatic Extrinsic Calibration of LIDAR and Monocular Camera Images

Mobile robots are being equipped with an ever increasing amount of sensors, and by taking advantage of each modality's forte, through sensor fusion we are able to produce superior results for higher level functions such as pedestrian detection. An accurate calibration between the sensors, however, is essential for obtaining a correct fusion. One of the more common setups is that of an optical camera paired with a lidar system, complementing each other's data. While the camera offers rich color information through a dense image, the lidar produces high accuracy, though sparse depth measurements. In this paper we will present two methods for estimating the 6 degrees of freedom parameters necessary to extrinsically calibrate the two sensors, without the use of special calibration targets: the first one attempts to align edges detected in both the lidar point cloud and the corresponding color image, while the second approach minimizes the depth difference between the measured lidar data and a depth map generated from the corresponding monocular image. By defining correlations between the two modality's information, we are able to construct an optimization problem on the 6 calibration parameters. Our experiments show good results when matched against similar solutions proposed in the literature, especially in the case of the calibration algorithm based on depth estimation.
Decision Support System for Determining Academic Advisor Using Simple Additive Weighting

In every college there is always an Academic Advisor. Every student studying in a tertiary institution must have an Academic Advisor. Higher education is also a continuation of secondary education which is organized to prepare students to become members of the community who have the ability, not only in the academic field but also in all fields where students or student forms a form of agent of change, agent of control, and iron stock. The problem that occurs in determining the Academic Advisor is the need for Academic Advisors for students or students at STMIK Pringsewu Lampung. Where the parties concerned are still confused in determining the Academic Advisor, who is eligible to be an Academic Advisor? This problem can be solved by the method of saw in determining criteria and decision-making. This method will give an alternative weighting where the biggest weight is the alternative choice who will be determined to be Academic Supervisor in STMIK Pringsewu Lampung College.
Privacy-Preserving eID Derivation for Self-Sovereign Identity Systems

As centralized identity management solutions amass identity data, they increasingly become attractive targets for cyber attacks, which entail consequences for users that range from service disruptions to exposure of sensitive user data. Self-sovereign identity (SSI) strives to return the control over identity data to the users by building on decentralized architectures. However, the adoption of SSI systems is currently hampered by a lack of qualified identity data that satisfies the services’ requirements. Additionally, there is a gap w.r.t the user’s privacy: Intermediate components (e.g., importers or SSI network nodes) learn the users’ sensitive attributes during the derivation of eID data. In this work, we present a decentralized eID derivation concept that preserves the users’ privacy while maintaining the data’s trustworthiness without revealing the plain data to any component outside the users’ control. Our proposed system also enables users to selectively disclose only relevant parts of the imported identity assertion according to the service’s requirements. We also implement and evaluate a proof-of-concept to demonstrate the feasibility and performance of our concept.
Sparse support vector machine with pinball loss

Abstract The standard support vector machine (SVM) with a hinge loss function suffers from feature noise sensitivity and instability. Employing a pinball loss function instead of a hinge loss function in SVMs provides noise insensitivity to the model as it maximizes the quantile distance. However, the pinball loss function simultaneously causes the model to lose sparsity by penalizing correctly classified samples. To overcome the aforementioned shortcomings, we propose a novel sparse SVM with pinball loss (Pin‐SSVM) for solving classification problems. The proposed Pin‐SSVM employs L1‐norm in the SVM classifier with the pinball loss (Pin‐SVM), which ensures the robustness, sparseness, and noise insensitivity of the model. The proposed Pin‐SSVM eradicates the need to solve the dual as we simply obtain its solution by solving a linear programming problem (LPP). The proposed Pin‐SSVM does not spend more computational time as that of Pin‐SVM. Hence, solving an LPP with two linear inequality constraints does not affect the computational complexity. The numerical experiments on several real‐world benchmark noise corrupted and imbalanced UCI datasets demonstrate that the proposed Pin‐SSVM is suitable for noisy and imbalanced data sets, and in most cases, outperforms the results of the baseline models.
A Coordinated Path Planning Algorithm for Multi-Robot in Intelligent Warehouse

In this paper, we study the coordinated path planning problem of multi-robot in intelligent warehouse. We first describe the problem of the multi-robot path planning and analyze the basic structure of the intelligent warehouse system. Then we discuss the adjustment of multi-robot path planning algorithm from two aspects. We reserve the path information of the continuous time period in the reservation table of the Windowed Hierarchical Cooperative A* (WHCA*) algorithm, and analyze whether to reuse the path information each time the algorithm is invoked. Then on the basis of this, we introduce the turning factor as a measure when expanding a path, and hope to find the path with fewer turns to reduce the travel time of the robot. Finally, we do some experiments and make the analysis. The results show that the methods we used can improve the path planning efficiency of the robot and increase the order throughput of the intelligent warehouse system.
Underwater Image Enhancement Based on the Iteration of a Generalization of Dark Channel Prior

Underwater image enhancement is important for images captured in underwater because underwater images often suffer from color cast, low contrast and degraded visibility due to the absorption and scattering of light in water. In this paper, we propose a novel algorithm for underwater image restoration based on a generalization of the dark channel prior (GDCP). Though there are various types of underwater images, we especially focus on underwater images with depth because these images are not enhanced well by current algorithms. The proposed algorithm is composed of the iteration of GDCP and image fusion. Additionally, we introduce the new ambient light estimation to adapt to more types of images. Experimental results show that proposed algorithm is effective for various types of underwater images, especially for the images with depth.
Possiblistic C-Means Clustering on Directional Data

Statistical analysis of directional data has a long history in the literature where probability distributions, such as von Mises, Fisher, wrapped Cauchy, and wrapped normal, have been used to model data that indicates directional orientations. This paper aims to analyze these data through clustering algorithms. Most of clustering algorithms are based on mixture distribution models or extensions of the k-means algorithm, such as fuzzy c-means (FCM) and possibilistic c-means (PCM). In this paper, we propose PCM direction (PCM-D) algorithm for clustering directional data which is not necessarily based on mixture distribution models by extending PCM to directional data. The proposed PCM-D can perform well for circular, spherical and even hyperspherical data. Comparisons are made with some existing directional clustering algorithms through simulations to show its efficiency. PCM-D was also applied in clustering real directional data.
An empirical evaluation of three-dimensional pie charts with individually extruded sectors in a geovisualization context

This study compares participants’ performance in extracting information from three-dimensional pie charts with individually extruded sectors in a single map frame against two-dimensional pie and bar charts in adjacent map frames. Specifically, we examine the response accuracy and response times of 181 adults who were asked to (1) identify the highest magnitude, (2) estimate a proportion, (3) perform both at the same time, and (4) solve a map-related task using the two chart setups. For each task, charts were shown on backgrounds with increasing visual complexity: a blank, a borders-only, and a choropleth map. Furthermore, we tested whether participants’ performance improved through additional practice with the two chart types. We did not observe any differences in participants’ aggregated response accuracy or response times between the tested three-dimensional and two-dimensional chart types for the (1) highest magnitude task and (2) proportion task. However, participants solved the (3) combination task with two-dimensional pie and bar charts on a blank background more accurately and were faster in fulfilling the (4) spatial task with three-dimensional pie charts. The first difference, however, leveled for participants who gained more practice and who accomplished the combination task on maps with higher visual complexity, whereas the second difference persisted even for more trained subjects.
A Design Model for a Degree Programme in Cyber Security

The need for skillful cyber security workforce has increased dramatically during the last ten years. The contents of the degree programmmes have not been able to respond to this need adequately and the curriculum contents have not always met the industry's knowledge needs.