Reviewerly/exHarmony huggingface dataset:
An IoT Inspired Distributed Data Fusion Architecture for Coastal Surveillance Applications

In this paper, we address the distributed data fusion problem considering a real scenario with multiple sensor sites geographically scattered around a bay. The advance of IoT, with more and more objects being connected, delivering and sharing huge amount of data, represents a big challenge. A timely fusion, using data obtained from different sources, like IoT and others, to provide efficient, reliable and accurate information to the decision makers is a requirement of modern data fusion systems. The paper presents a conceptual approach for a distributed data fusion system, applied in a maritime environment, where the common operational picture is obtained through a tactical datalink network. The concept and implementation of the proposed system follows the paradigm of Network Centric Warfare, which is an information age theory of warfare.
Meeting of the Talent & Expertise Development Platform at ÖBB, Vienna from 4 – 5 April 2019 – UIC Communications

The spring meeting of the UIC Talent & Expertise Development Platform (TEDP) which took place at the kind invitation of OBB, was held in Vienna on 4 and 5 April October 2019 and brought together…
Library Licensing: A Manual for Busy Librarians

One of the many challenges librarians face is reviewing and negotiating licensing agreements. Halaychik and Reagan discuss these challenges in Library Licensing: A Manual for Busy Librarians. The scope of this book is much broader than the title seems to indicate, covering not only license-related topics regarding electronic resources in its six chapters, but also information on contract management software, Integrated Library Systems (ILS) and maintenance, and services agreements. For this reason, the book can feel unfocused at times. Chapter 1 briefly introduces basic information about contracts, while chapters 2 through 6 give a broad overview of the mechanics of a contract (including common clauses within contracts) and information concerning organizing, managing, and administering contracts, with examples of checklists and contracts in chapter 5 and subsequent appendixes.
A simplified pipeline for line segment detection

A real-time CNN-based method is proposed to detect line segments of a given image with a simple and compact pipeline, named as Center-based Line Segment Detector (CenterLine). A line segment is modeled by its center-point with two offsets along the x-axis and y-axis. Under that representation, the localization, direction and length of a line segment can be encoded simultaneously. The method uses keypoint estimation to find the center point of a line and regresses its offsets. Comprehensive experimental results on the Wireframe dataset and the YorkUrban dataset show that the proposed method achieves state-of-the-art performance with 49 FPS in inference speed, which is outperforming most of the existing line segment detectors. With good performance and rapidity, our method is applicable to practical systems with real-time requirements.
Emotion Detection on Twitter Textual Data

Textual data is one of the most important types of data nowadays. With the use of social media like Twitter and Facebook, the amount of textual data is growing at an unimaginable speed. To fully use these data, it is very important to extract useful information from it as much as possible. While some information can be directly extracted from the text, other information needs to be discovered with an advanced method. For instance, the feelings of authors while the text was written, or the emotions that authors wanted to express. This leads to the use of sentiment analysis. Recently, sentiment analysis has drawn attention from many researchers. With the growth of online resources of textual data, the ability to detect different emotions from textual data is becoming very useful. In this paper, we present an application that can detect four distinct emotions from Twitter posts. They are joy, anger, fear, and sadness. The evaluation result shows the accuracy and applicability of our application.
VRIF Guidelines on Live VR Services

Founded at the Consumer Electronics Show in 2017, the Virtual Reality Industry Forum (VRIF) ( <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">www.vr-if.org</uri> ) is chartered with the mission to further the widespread availability of high-quality audiovisual virtual reality (VR) experiences. Its charter includes delivering guidelines and advocating industry consensus around common technical standards for end-to-end VR interoperability from content production through distribution to consumption. VRIF delivered the first release of its Guidelines <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> in early 2018 detailing an initial set of best practices and recommendations for realizing high-quality, interoperable 360° video experiences. Its latest Guidelines version 2.2 (2020) includes new features such as live VR services, high dynamic range (HDR) support, use of text and fonts, and watermarking for theatrical VR. Currently, VRIF is working on new features around volumetric content production and distribution, cloud VR and edge computing, enhanced 360° video support, and enabling social VR experiences.
Distributed Unscented Filter with Minimum Sigma Set for Improved Performance

This paper investigates the distributed state estimation of nonlinear dynamical systems with stochastic noises. To improve the unscented filtering with minimum sigma set, weighted average consensus is adopted in the form of non-square root and square-root. Compared with the traditional unsented Kalman filter with 2n + 1 sigma points on n dimensional systems, the non-square root algorithm can reduce the load of computations. Furthermore, the square-root algorithm can avoid the divergence of non-square root. Finally, the numerical simulation on a target tracking scenario is performed to verify the superority of the two novel distributed unscented filters over the exsiting one.
Wide-sense 2-frameproof codes

Various kinds of fingerprinting codes and their related combinatorial structures are extensively studied for protecting copyrighted materials. This paper concentrates on one specialised fingerprinting code named wide-sense frameproof codes in order to prevent innocent users from being framed. Let Q be a finite alphabet of size q. Given a t-subset $$X=\{ x ^1,\ldots , x ^t \}\subseteq Q^n$$ , a position i is called undetectable for X if the values of the words of X match in their ith position: $$x_i^1=\cdots =x_i^t$$ . The wide-sense descendant set of X is defined by $${{\text {wdesc}}}(X)=\{y\in Q^n:y_i=x_i^1,i\in {U}(X)\},$$ where U(X) is the set of undetectable positions for X. A code $$\mathcal{C}\subseteq Q^n$$ is called a wide-sense t-frameproof code if $${{\text {wdesc}}}(X) \cap \mathcal{C} = X$$ for all $$X \subseteq \mathcal{C}$$ with $$|X| \le t$$ . The paper improves the upper bounds on the sizes of wide-sense 2-frameproof codes by applying techniques on non 2-covering Sperner families and intersecting families in extremal set theory.
The Spline Multi-Target Multi-Bernoulli Filter

A B-Spline implementation of the multi-target multi-Bernoulli (MeMBer) filter for nonlinear Gaussian/non-Gaussian models is proposed. Specifically, the spatial PDF (SPDF) of each Bernoulli component in the MeMBer density is represented by a B-Spline curve, which is characterized by the spline knots and control points. The spline knots and control points are then propagated via prediction and update steps of the MeMBer filter. Besides, a revised fitting algorithm is proposed so as to improve the implementation efficiency. The effectiveness of the proposed method is assessed via simulation experiments.
RGB‐D salient object detection via deep fusion of semantics and details

Abstract In this paper, we address RGB‐D salient object detection task by jointly leveraging semantics and contour details of salient objects. We propose a novel semantics‐and‐details complementary fusion network to adaptively integrate cross‐model and multilevel features. Specifically, we employ two kinds of fusion modules in our model, which are designed for fusing high‐level semantic features and integrating contour detail features of the scene components, respectively. The semantics fusion module aggregates high‐level interdependent semantic relationships by a nonlinear weighted summation of small and medium receptive fields. Meanwhile, the details module integrates multi‐level contour detail features to leverage expressive details of salient objects. We achieve new state‐of‐the‐art salient object detection results on seven RGB‐D datasets, that is, STERE, NJU2000, LFSD, NLPR, SSD, DES, and SIP2019 dataset. Experimental results demonstrate that our method outperforms eleven state‐of‐the‐art salient object detection methods.
RandomLink – Avoiding Linkage-Effects by Employing Random Effects for Clustering

We present here a new parameter-free clustering algorithm that does not impose any assumptions on the data. Based solely on the premise that close data points are more likely to be in the same cluster, it can autonomously create clusters. Neither the number of clusters nor their shape has to be known. The algorithm is similar to SingleLink in that it connects clusters depending on the distances between data points, but while SingleLink is deterministic, RandomLink makes use of random effects. They help RandomLink overcome the SingleLink-effect (or chain-effect) from which SingleLink suffers as it always connects the closest data points. RandomLink is likely to connect close data points but is not forced to, thus, it can sever chains between clusters. We explain in more detail how this negates the SingleLink-effect and how the use of random effects helps overcome the stiffness of parameters for different distance-based algorithms. We show that the algorithm principle is sound by testing it on different data sets and comparing it with standard clustering algorithms, focusing especially on hierarchical clustering methods.
Fortification of Policing in Nigeria Using ICT Backbone for Strategic Competitive Advantage

This chapter discusses fortification of policing in Nigeria leveraging information communication and technology (ICT) backbone for strategic competitive advantage. The need to embrace ICT-enhanced policing in Nigeria becomes imperative because insecurity, robbery, kidnapping, terrorism, and insurgency have taken a new dimension and have gone sophisticated as criminal elements have deployed high-tech approach such as mobile technology and internet technology for exploiting and unleashing criminal activities on the society. To reduce incessant and unpleasant proliferation of modern crime, the situation calls for the fortification of existing policing approach in Nigeria using information communication technologies. This exploratory study is an attempt at strengthening the traditional policing approach to be able to meet the insecurity challenges currently being faced and facing the society. The implication of the study is that the incidences of insecurity, terrorism, and insurgency can effectively be rendered prostrate and managed in effectively through the use and application of ICT. Notably among the proposal for an ICT-enhanced policing is the concept of virtual community policing that explores the availability of mobile devices for easy and effective crime reporting and crime control in Nigeria.
A Novel Keypoints-Based Image Registration Method with Fully Convolutional Neural Network

Image registration plays an important role in image processing. The key-points based registration technology is a research hotspot. However, the quality of key-points detection will greatly affect the performance of image registration. In this paper, we use the fully convolutional neural network to perform key-points detection and feature extraction on the image, which is applied to the registration. Experiments show that the proposed method achieves better performance and is superior to the traditional key-points based registration method.
The Curious Case of Session Identification

Dividing interaction logs into meaningful segments has been a core problem in supporting users in search tasks for over 20 years. Research has brought up many different definitions: from simplistic mechanical sessions to complex search missions spanning multiple days. Having meaningful segments is essential for many tasks depending on context, yet many research projects over the last years still rely on early proposals. This position paper gives a quick overview of session identification development and questions the widespread use of the industry standard.
Conditional Reasoning and Relevance

The Weak Completion Semantics is a computational and nonmonotonic cognitive theory based on the three-valued logic of Łukasiewicz. It has been applied to adequately model – among others – the suppression task, the selection task, syllogistic reasoning, and conditional reasoning. In this paper we investigate the case where the antecedent of a conditional is true, but its consequent is unknown. We propose to apply abduction in order to find an explanation for the consequent. This allows to derive new conditionals which are necessarily true. But it also leads to two problems, viz. that consequents should not abduce themselves and that the antecendent of a conditional should be relevant to its consequent. We propose solutions for both problems.
Spatial-Temporal Co-attention Network for Action Recognition

In the traditional two-stream convolutional neural network, RGB image and optical flow image are trained separately and then simply fused, which results in the lack of fine-grained interaction between static image and dynamic clue, making the important clue of timing information unable to play its full role in the task of video action recognition. We propose a spatial-temporal co-attention network, which allows the attention of spatial features and temporal features to guide each other and collect basic information from the two features. The network utilizes the attention mechanism to estimate the commonality between static images and temporal information by focusing on their shared common semantics. Our experiments verify the effectiveness of the spatial-temporal co- attention network in combining spatial information and temporal information, and obtain better performance on three public standard datasets.
Remote Sensing Image Zero-Watermarking Algorithm Based on Accurate Polar Harmonic Fourier Moment

This paper introduces a remote zero-watermarking algorithm based on polar harmonic Fourier moments (PHFM) and Logistic chaotic mapping. In this algorithm, Gaussian numerical integration is used to calculate the PHFM of the original gray image and construct a binary feature image. Finally, the binary feature image is XOR with the scrambled logo image to obtain a zero-watermarking image. When copyright protection is verified, the logo image obtained by XORing the binary feature image and the image to be verified is compared with the original logo image to determine the copyright ownership of the image. The experimental results show that compared with other zero-watermarking algorithms, the proposed algorithm is robust to both conventional image attacks and geometric attacks.
Collaborative Robotic Manipulation: A Use Case of Articulated Objects in Three-dimensions with Gravity

This paper addresses two intertwined needs for collaborative robots operating in shop-floor environments. The first is the ability to perform complex manipulation operations, such as those on articulated or even flexible objects, in a way robust to a high degree of variability in the actions possibly carried out by human operators during collaborative tasks. The second is encoding in such operations a basic knowledge about physical laws (e.g., gravity), and their effects on the models used by the robot to plan its actions, to generate more robust plans. We adopt the manipulation in three-dimensional space of articulated objects as an effective use case to ground both needs, and we use a variant of the Planning Domain Definition Language to integrate the planning process with a notion of gravity. Different complexity levels in modelling gravity are evaluated, which tradeoff model faithfulness and performance. A thorough validation of the framework is done in simulation using a dual-arm Baxter manipulator.
Population counting method based on semantic perceptual network

The latest method to calculate the number of people in a crowded scene depends on the depth network to estimate the population density. They usually use the same filter on the whole image or large image block. Only in this way can they estimate the local scale to compensate for perspective distortion. This is usually selected by training auxiliary classifiers. For predefined image patches, the best kernel size is selected in a limited set. As a result, these methods are not end-to-end trainable and are limited in the context in which they can be leveraged. This paper introduces an end-to-end trainable depth structure, which uses multiple-scale cores to capture image context semantic information, and learns the importance of each feature in each image location. In other words, our method adaptively encodes the scale of context information needed to accurately predict population density. This results in an algorithm superior to the most advanced crowd counting method, especially when the perspective effect is strong.
Study of a Cleaning Intelligent Control System for Rice and Wheat Combine Harvester

The intelligent regulation and control strategies for rice and wheat combine harvesters' operation are lacking and the rule of parameter matching is fuzzy in China, around these issues, the dynamic correlation control law among the parameters of rice and wheat, cleaning operation parameters of combine harvesters, the cleaning loss rate and impurity rate are studied, and so on. The intelligent control model for the rice and wheat combine harvester is established based on case-based reasoning. According to different rice and wheat varieties, water content and other rice and wheat properties, the control scheme of cleaning fan speed, air distributor plate angle and upper sieve opening with low cleaning impurity rate and cleaning loss rate is provided. Through the development of web-based cleaning intelligent control expert system and conduct experimental evaluation, the feasibility and effectiveness of the case-based reasoning method in the intelligent control filed of rice and wheat combine harvesters is verified.
Systematic Approach to Engineer Decentralized Self-adaptive Systems

Self-adaptation is a widely accepted approach to deal with uncertainties that are difficult to anticipate before deployment. We focus on architecture-based adaptation that relies on a feedback loop that reasons over architectural models of the system at runtime to make adaptation decisions. In particular, we study decentralized self-adaptive systems where self-adaptation is realized through multiple coordinating feedback loops. Such decentralization is crucial for systems where adaptation decisions cannot be made in a centralized way, such as in large scale Internet of Things (IoT). State of the art in this area is limited to either conceptual ideas or solutions dedicated to particular settings. This paper outlines a research project targeting the research question: “how to model and realize decentralized feedback loops that are capable to guarantee compliance of system goals in an efficient way despite uncertainties the system faces?” We plan to answer this question in two stage. First, we study commonalities and variability of decentralized self-adaptive systems leveraging on patterns and coordination mechanisms, and reify our insights in a framework. Second, we study language support for the design and implementation of decentralized self-adaptation, capitalizing on the outcome of the first stage. To ensure guarantees for the qualities we will found our work on formal techniques. To ensure efficiency, we will combine statistical techniques with machine learning. We plan to validate the research results in two domains: IoT and multi-cloud systems.
Blockchain for Electronic Voting Purposes

The aim of this document is to describe the potential opportunities of implementing blockchain technology for electronic voting purposes, which may help reduce the costs of traditional voting systems (on-site, off-site), as well as the potential effects that it may have in electoral outcomes. This work also describes the initiatives adopted by some countries that have used this technology in electoral processes seeking to improve quality, confidence, transparency, and security.
GSSM: An Integration Model of Heterogeneous Factors for Point-of-Interest Recommendation

Millions of users prefer to share their locations and social relationships on location-based social networks (LBSNs), such as Gowalla, Foursquare, and etc. It has become an important function to recommend points-of-interest (POIs) for users based on their check-in records. However, the user-location matrix generated by check-in records is often very sparse which causes those traditional methods to present a suboptimal recommendation performance. We proposed a new POI recommendation algorithm named GSSM, which combines multiple factors which can help solve problems in these recommendations and improve recommendation performance. We employ the gaussian mixture model (GMM) to extract geographical factor from user’s check-in records. Then we assign a concrete semantics to define social factor. The strength of the connection between users is indicated by the number of visits from associated users. In order to capture the similarity factor between users, we apply the popular cosine similarity to improve recommendation performance for the POI recommendation as the basis of calculating user similarity. Finally, an optimal fusion framework is designed to incorporate the three factors mentioned above, and GSSM is proposed. We conduct experiments on large-scale real data set which is crawled from Gowalla. The experimental results show that the performance of GSSM is better than the baseline and many current popular POI recommendation models.
Research on improved sharpening algorithm based on closed operation and binarization

Abstract In this paper, binarization and closed operation are used to improve traditional sharpening algorithms. Aiming at the problem that traditional sharpening algorithm tends to sharpen noise when processing pictures. First of all, this paper eliminates noise through binarization, and then uses morphological closed operation to fill the cavities generated by noise. Finally, the Sobel operator is used to detect the edge of the image, which is superimposed with the original image and simulated on matlab. The simulation result shows that the improved sharpening algorithm based on binarization and closed operation can not only sharpen the edges of the image, but also not sharpen the noise. The innovation of this paper is to make the image sharpening effect better than traditional phase algorithm.
Engineering Multiagent Organizations Through Accountability

In my PhD I have been investigating the notions of accountability and responsibility in multiagent organizations. The main objective of the work is to develop both a conceptual model and a programming framework encompassing the two concepts as engineering tools, which, in our view, are fundamental for the realization of robust systems.