Reviewerly/exHarmony huggingface dataset:
Künstliche Intelligenz in der Bildanalyse – Grundlagen und neue Entwicklungen

Since 2017, there have been several reports of artificial intelligence (AI) achieving comparable performance to human experts on medical image analysis tasks. With the first ratification of a computer vision algorithm as a medical device in 2018, the way was paved for these methods to eventually become an integral part of modern clinical practice.The purpose of this article is to review the main developments that have occurred over the last few years in AI for image analysis, in relation to clinical applications and dermatology.Following the annual ImageNet challenge, we review classical methods of machine learning for image analysis and demonstrate how these methods incorporated human expertise but failed to meet industrial requirements regarding performance and scalability. With the rise of deep learning based on artificial neural networks, these limitations could be overcome. We discuss important aspects of this technology including transfer learning and report on recent developments such as explainable AI and generative models.Deep learning models achieved performance on a par with human experts in a broad variety of diagnostic tasks and were shown to be suitable for industrialization. Therefore, current developments focus less on further improving accuracy but rather address open issues such as interpretability and applicability under clinical conditions. Upcoming generative models allow for entirely new applications.Deep learning has a history of remarkable success and has become the new technical standard for image analysis. The dramatic improvement these models brought over classical approaches enables applications in a rapidly increasing number of clinical fields. In dermatology, as in many other domains, artificial intelligence still faces considerable challenges but is undoubtedly developing into an essential tool of modern medicine.
Inequality and financial development: Evidence from selected MENA region countries

Abstract The paper investigates the impact of financial development on income inequality for selected 11 MENA region countries over the period 1990 to 2015. Pooled Mean Group Estimation of dynamic heterogeneous panels is implemented to examine the relationship between the variables. Furthermore, we employed panel cointegration to test for the existence of a long‐run relationship between the variables. The findings shows a significant linear long‐run negative relationship between financial development and income inequality. The outcomes are in consistency with the inequality‐narrowing hypothesis by Banerjee and Newman (1993), Galor and Zeria (1993) and Mookherjee and Ray (2003). Thus we conclude that financial development is an important determinant for inequality reduction for these countries.
Tyre pattern image retrieval – current status and challenges

Tyre pattern image retrieval (TPIR) is an important tool in the investigation of criminal activities and traffic accidents. Although content-based image retrieval (CBIR) has been developed for decades with abundant results, the study on TPIR which started in the 1990s has not made much progress. The lack of large standard test datasets is a crucial shortcoming which limits the research in this field. Information presented in this paper is a result of the authors’ literature research on recent academic publications and practical field investigation in the public security and transportation sectors. The state-of-the-art technologies in the field of TPIR are surveyed in detail from two aspects of tyre patterns – their low-level spatial features and high-level semantic features. Existing algorithms are examined and their pros and cons are compared and verified through experimental results. This paper also surveys the available tyre pattern datasets used in all available literature. Finally, with the considerations on technology trends in image retrieval and application requirements in TPIR, the future research directions in this field are laid out.
Blockchain Ethics: A Systematic Literature Review of Blockchain Research

Blockchain is a recent development in technology which allows a cryptographically secured, decentralised and distributed storage of data. The technology innovation was done as a part of and became familiar through Bitcoin cryptocurrency, where it is used to openly store currency transactions among its users. Whereas a majority of recent research and development work of blockchain has been in a number of different cryptocurrency applications, the technology itself has been proposed and used in various domains ranging from open contracts to electronic voting. As the technology has potentially a remarkable power to shape the modern digital societies, its usage possesses several ethical questions. To study the state of the art in blockchain ethics research, this study present a systematic literature study (n = 26) on the phenomenon. The results show that the area is swiftly maturing, yet there is a call for concrete usable tools—for the practitioners and scholars—and deeper understanding of relevant ethical concerns.
Structural Information Reconstruction of Distorted Underwater Images Using Image Registration

Imaging through wavy air-water surface suffers from uneven geometric distortions and motion blur due to surface fluctuations. Structural information of distorted underwater images is needed to correct this in some cases, such as submarine cable inspecting. This paper presents a new structural information restoration method for underwater image sequences using an image registration algorithm. At first, to give higher priority to structural edge information, a reference frame is reconstructed from the sequence frames by a combination of lucky patches chosen and the guided filter. Then an iterative robust registration algorithm is applied to remove the severe distortions by registering frames against the reference frame, and the registration is guided towards the sharper boundary to ensure the integrity of edges. The experiment results show that the method exhibits improvement in sharpness and contrast, especially in some structural information such as text. Furthermore, the proposed edge-first registration strategy has faster iteration velocity and convergence speed compared with other registration strategies.
Design of unscented Kalman filter based on the adjustments of the number and placements of the sampling points

This paper addresses a general sampling method of the unscented Kalman filter (UKF) for nonlinear state estimation. The sampling method for standard UKF is analyzed, and we propose a theorem to address the conditions that UKF provides a third order accuracy in terms of Taylor series expansion for expectation estimation by changing the number and placements of the sampling points. This theorem can be used to develop new UKF. Based on this theorem, we propose a method to design the placements of the sampling points, including the directions and lengths by optimization strategies. Simulation studies demonstrate that the proposed UKF is effective and can significantly improve the filter performance.
Gamified service exchange platform on blockchain for IoT business agility

In order to enable IoT solution providers to quickly adapt to market dynamics and improve business agility, in this paper we introduce the concept of a decentralized gamified service exchange platform where the solution providers can dynamically offer and request services in an autonomous peer-to-peer fashion. Cost and decision to exchange services are set during operation time based on gamification policies according to business goals. The proposed concept is based on blockchain technology to provide a tokenized economy where the IoT solution providers can implement gamification techniques using smart contracts to maximize profits during service offering and requesting.
Probability Distribution-Based Dimensionality Reduction on Riemannian Manifold of SPD Matrices

Representing images and videos with Symmetric Positive Definite (SPD) matrices and utilizing the intrinsic Riemannian geometry of the resulting manifold has proved successful in many computer vision tasks. Since SPD matrices lie in a nonlinear space known as a Riemannian manifold, researchers have recently shown a growing interest in learning discriminative SPD matrices with appropriate Riemannian metrics. However, the computational complexity of analyzing high-dimensional SPD matrices is nonnegligible in practical applications. Inspired by the theory of nonparametric estimation, we propose a probability distribution-based approach to overcome this drawback by learning a mapping from the manifold of high-dimensional SPD matrices to a manifold of lower-dimension, which can be expressed as an optimization problem on the Grassmann manifold. Specifically, we perform the dimensionality reduction for high-dimensional SPD matrices with popular Riemannian metrics and an affinity matrix constructed using an estimated probability distribution function (PDF) to achieve maximum class separability. The evaluation of several classification tasks shows the competitiveness of the proposed approach compared with state-of-the-art methods.
Dimension Reduction for Outlier Detection Using DOBIN

This article introduces DOBIN, a new approach to select a set of basis vectors tailored for outlier detection. DOBIN has a simple mathematical foundation and can be used as a dimension reduction tool for outlier detection tasks. We demonstrate the effectiveness of DOBIN on an extensive data repository, by comparing the performance of outlier detection methods using DOBIN and other bases. We further illustrate the utility of DOBIN as an outlier visualization tool. The R package dobin implements this basis construction. Supplementary materials for this article are available online.
Research on Edge Detection Method Based on Improved HED Network

Abstract Aiming at the problem of rough and fuzzy edges generated by the current edge detection technology based on HED network, an improved edge detection method for HED network is proposed. First, the useful information captured by each convolutional layer of HED becomes rougher as the size of the acceptance field increases. The improved HED network makes use of all the information of the convolution layer to capture more targets in a larger range, or make the local boundaries of the targets possible. The improved HED makes full use of the multi-scale and multi-level information of the target to obtain high-precision and high-quality edge maps, which lays a good foundation for image segmentation.
Participatory design for ontologies: a case study of an open science ontology for qualitative coding schemas

Purpose The open science movement calls for transparent and retraceable research processes. While infrastructures to support these practices in qualitative research are lacking, the design needs to consider different approaches and workflows. The paper bases on the definition of ontologies as shared conceptualizations of knowledge (Borst, 1999). The authors argue that participatory design is a good way to create these shared conceptualizations by giving domain experts and future users a voice in the design process via interviews, workshops and observations. Design/methodology/approach This paper presents a novel approach for creating ontologies in the field of open science using participatory design. As a case study the creation of an ontology for qualitative coding schemas is presented. Coding schemas are an important result of qualitative research, and reuse can yield great potential for open science making qualitative research more transparent, enhance sharing of coding schemas and teaching of qualitative methods. The participatory design process consisted of three parts: a requirement analysis using interviews and an observation, a design phase accompanied by interviews and an evaluation phase based on user tests as well as interviews. Findings The research showed several positive outcomes due to participatory design: higher commitment of users, mutual learning, high quality feedback and better quality of the ontology. However, there are two obstacles in this approach: First, contradictive answers by the interviewees, which needs to be balanced; second, this approach takes more time due to interview planning and analysis. Practical implications The implication of the paper is in the long run to decentralize the design of open science infrastructures and to involve parties affected on several levels. Originality/value In ontology design, several methods exist by using user-centered design or participatory design doing workshops. In this paper, the authors outline the potentials for participatory design using mainly interviews in creating an ontology for open science. The authors focus on close contact to researchers in order to build the ontology upon the expert's knowledge.
Rational Exchange: Incentives in Atomic Cross Chain Swaps

Protocols based on Hash Time Lock Contracts (HTLCs) show tremendous potential to enable a truly decentralized exchange of digital currencies. In contrast to traditional financial systems and centralized crypto-exchanges, HTLC protocols provide a method to trade crypto-currencies in a peer-to-peer manner. However, considering the elimination of a third-party authority, remarkable price fluctuations and the protocol's extensive time-to-completion, involved parties might be incentivized to deviate from the protocol and cancel a trade midway. Thus, this paper analyzes the protocol's incentive structure based on a model of rationality to further quantify its impact on potential trades, using historical exchange rates. By analyzing different crypto-currency trading pairs, this paper highlights the probabilistic nature of a typical HTLC protocol. The results show that although the protocol does not offer a guarantee for a successful trade, it is applicable in scenarios of exchange-rates with low drift, low volatility, and an optimized time-to-completion.
Are You Confident That You Have Successfully Generated Adversarial Examples?

Deep neural networks (DNNs) have seen extensive studies on image recognition and classification, image segmentation, and related topics. However, recent studies show that DNNs are vulnerable in defending adversarial examples. The classification network can be deceived by adding a small amount of perturbation to clean samples. There are challenges when researchers want to design a general approach to defend against a wide variety of adversarial examples. To solve this problem, we introduce a defensive method to prevent adversarial examples from generating. Instead of designing a stronger classifier, we built a more robust classification system that can be viewed as a structural black box. After adding a buffer to the classification system, attackers can be efficiently deceived. The real evaluation results of the generated adversarial examples are often contrary to what the attacker thinks. Additionally, we do not assume a specific attack method premise. This incognizance to underlying attacks demonstrates the generalizability of the buffer to potential adversarial attacks. Extensive experiments indicate that the defense method greatly improves the security performance of DNNs.
A Survey Investigating the Combination and Number of IMUs on the Human Body Used for Detecting Activities and Human Tracking

In the present paper we give a classification of the position and the number of inertial measurement units (IMU) used to detect human activities and position tracking based on literature review. It presents a separate view of IMU position and IMU numbers placed on the human body. In total 60 relevant studies were found which focused on human activity recognition (HAR) and tracking with dead-reckoning. The focus lies on the number, the sample frequencies and the positioning of IMUs. To the best of our knowledge, this is the first work which looks particularly at the number and position of IMUs. The result shows that a comparison between the different studies fails, because the description of the positions is not precise enough and not uniform.
Vehicle Detection with Self-Training for Adaptative Video Processing Embedded Platform

Traffic monitoring from closed-circuit television (CCTV) cameras on embedded systems is the subject of the performed experiments. Solving this problem encounters difficulties related to the hardware limitations, and possible camera placement in various positions which affects the system performance. To satisfy the hardware requirements, vehicle detection is performed using a lightweight Convolutional Neural Network (CNN), named SqueezeDet, while, for tracking, the Simple Online and Realtime Tracking (SORT) algorithm is applied, allowing for real-time processing on an NVIDIA Jetson Tx2. To allow for adaptation of the system to the deployment environment, a procedure was implemented leading to generating labels in an unsupervised manner with the help of background modelling and the tracking algorithm. The acquired labels are further used for fine-tuning the model, resulting in a meaningful increase in the traffic estimation accuracy, and moreover, adding only minimal human effort to the process allows for further accuracy improvement. The proposed methods, and the results of experiments organised under real-world test conditions are presented in the paper.
Infinite families of 2-designs from two classes of binary cyclic codes with three nonzeros

&lt;p style='text-indent:20px;'&gt;Combinatorial &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ t $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-designs have been an interesting topic in combinatorics for decades. It is a basic fact that the codewords of a fixed weight in a code may hold a &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ t $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-design. Till now only a small amount of work on constructing &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ t $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-designs from codes has been done. In this paper, we determine the weight distributions of two classes of cyclic codes: one related to the triple-error correcting binary BCH codes, and the other related to the cyclic codes with parameters satisfying the generalized Kasami case, respectively. We then obtain infinite families of &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$ 2 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-designs from these codes by proving that they are both affine-invariant codes, and explicitly determine their parameters. In particular, the codes derived from the dual of binary BCH codes hold five &lt;inline-formula&gt;&lt;tex-math id="M5"&gt;\begin{document}$ 3 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-designs when &lt;inline-formula&gt;&lt;tex-math id="M6"&gt;\begin{document}$ m = 4 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;.&lt;/p&gt;
Exploiting bi-directional global transition patterns and personal preferences for missing POI category identification

Recent years have witnessed the increasing popularity of Location-based Social Network (LBSN) services, which provides unparalleled opportunities to build personalized Point-of-Interest (POI) recommender systems. Existing POI recommendation and location prediction tasks utilize past information for future recommendation or prediction from a single direction perspective, while the missing POI category identification task needs to utilize the check-in information both before and after the missing category. Therefore, a long-standing challenge is how to effectively identify the missing POI categories at any time in the real-world check-in data of mobile users. To this end, in this paper, we propose a novel neural network approach to identify the missing POI categories by integrating both bi-directional global non-personal transition patterns and personal preferences of users. Specifically, we delicately design an attention matching cell to model how well the check-in category information matches their non-personal transition patterns and personal preferences. Finally, we evaluate our model on two real-world datasets, which clearly validate its effectiveness compared with the state-of-the-art baselines. Furthermore, our model can be naturally extended to address next POI category recommendation and prediction tasks with competitive performance.
WikiCSSH: Extracting Computer Science Subject Headings from Wikipedia

Domain-specific classification schemas (or subject heading vocabularies) are often used to identify, classify, and disambiguate concepts that occur in scholarly articles. In this work, we develop, apply, and evaluate a human-in-the-loop workflow that first extracts an initial category tree from crowd-sourced Wikipedia data, and then combines community detection, machine learning, and hand-crafted heuristics or rules to prune the initial tree. This work resulted in WikiCSSH; a large-scale, hierarchically-organized subject heading vocabulary for the domain of computer science (CS). Our evaluation suggests that WikiCSSH outperforms alternative CS vocabularies in terms of coverage of CS terms that occur in research articles. WikiCSSH can further distinguish between coarse-grained versus fine-grained CS concepts. The outlined workflow can serve as a template for building hierarchically-organized subject heading vocabularies for other domains that are covered in Wikipedia.
Evolutionary Dendritic Neural Model for Classification Problems

In this paper, an evolutionary dendritic neuron model (EDNM) is proposed to solve classification problems. It utilizes synapses and dendritic branches to implement the nonlinear computation. Distinct from the classical dendritic neuron model (CDNM) trained by the backpropagation (BP) algorithm, the proposed EDNM is trained by a metaheuristic cuckoo search (CS) algorithm instead, which has been regarded as a global searching algorithm. CS algorithm enables EDNM to avoid several disadvantages, such as slow convergence, trapping into local minimum, and being sensitive to initial values. To evaluate the performance of EDNM, we compare it with a multilayer perceptron (MLP) and CDNM on two benchmark classification problems. The experimental results demonstrate that EDNM is superior to MLP and CDNM in terms of accuracy rate, receiver operator characteristic curve (ROC), and convergence speed. In addition, the neural structure of EDNM can be replaced by a logical circuit completely, which can be implemented in hardware easily. The corresponding experimental results also verify the effectiveness of the logical circuit classifier.
Visual Saliency via Embedding Hierarchical Knowledge in a Deep Neural Network

Deep neural networks (DNNs) have been extensively applied in image processing, including visual saliency map prediction of images. A major difficulty in using a DNN for visual saliency prediction is the lack of labeled ground truth of visual saliency. A powerful DNN usually contains a large number of trainable parameters. This condition can easily lead to model over-fitting. In this study, we develop a novel method that overcomes such difficulty by embedding hierarchical knowledge of existing visual saliency models in a DNN. We achieve the objective of exploiting the knowledge contained in the existing visual saliency models by using saliency maps generated by local, global, and semantic models to tune and fix about 92.5% of the parameters in our network in a hierarchical manner. As a result, the number of trainable parameters that need to be tuned by the ground truth is considerably reduced. This reduction enables us to fully utilize the power of a large DNN and overcome the issue of over-fitting at the same time. Furthermore, we introduce a simple but very effective center prior in designing the learning cost function of the DNN by attaching high importance to the errors around the image center. We also present extensive experimental results on four commonly used public databases to demonstrate the superiority of the proposed method over classical and state-of-the-art methods on various evaluation metrics.