Reviewerly/exHarmony huggingface dataset:
Succinct Palette and Color Model Generation and Manipulation Using Hierarchical Representation

Abstract We propose a new method to obtain the representative colors and their distributions of an image. Our intuition is that it is possible to derive the global model from the local distributions. Beginning by sampling pure colors, we build a hierarchical representation of colors in the image via a bottom‐up approach. From the resulting hierarchy, we can obtain satisfactory palettes/color models automatically without a predefined size. Furthermore, we provide interactive operations to manipulate the results which allow the users to reflect their intention directly. In our experiment, we show that the proposed method produces more succinct results that faithfully represent all the colors in the image with an appropriate number of components. We also show that the proposed interactive approach can improve the results of applications such as recoloring and soft segmentation.
Towards A Marketplace for Multi-domain Cloud Network Slicing: Use Cases

Dynamic end-to-end cloud network slices following a Slice-as-a-Service (SaaS) paradigm offer promising opportunities in support of diverse vertical industries attached to different administrative domains. This paper presents a distributed Marketplace model to dynamically create cloud network slices involving different slice resource providers. Portrayed through the lens of three reference use case scenarios, we discuss potential attributes of having a Marketplace and the outcomes feeding the use case requirements within a Slice Marketplace scope. Effective selection, negotiation, and proper monitoring and enforcement of service level agreements between multiple administrative and technological domains are among the prime challenges towards the realization of a distributed Marketplace of federated network and cloud resource providers.
Optical inspection of solids of revolution by means of non-telecentric optics

Abstract The paper dwells upon automated inspection of solids of revolution on machines. It demonstrates how digital cameras with non-telecentric lenses generate the cross-section image. The paper presents an evaluation of error arising from using projection with non-telecentric (entocentric) optics for inspection. The analysis implies lenses with a focal length of 7, 12, or 25 mm. The obtained analytical dependencies of perspective error are used to adjust the measurements. Simulation confirms that errors can be compensated to values within the calibration error.
Cooperative Web Agents by Combining Semantic Technologies with Reinforcement Learning

An increasing number of Web pages are enriched with semantic annotations using, for instance, RDFa and Schema.org in order to ease data interpretation for Web browsers and search engines. Lifted data representations might enable a new class of semantic agents for complex decision-making on the Web, reaching from filling out Web forms and booking flights to mastering any Web-based task. However, exclusively using semantic models has several drawbacks, such as extensive manual efforts for enabling semantic agents to solve Web tasks or limited capabilities to personalize task solutions for end users. While using semantics is a crucial component for guiding prospective semantic agents, we have to find a balance between modelling rich background knowledge and learning optimal agent behaviour through advanced Machine Learning (ML) methods. In this work, we propose a semantic agent framework for (i) modelling agent-related semantic annotations for Web tasks, (ii) using the latter to train statistical agents using Reinforcement Learning (RL) in an offline simulation as well as in real online use and (iii) feeding back the learned agent behaviour and its provenance information in terms of a semantic model that can be directly used by purely semantics-based agents. We evaluate our approach based on the MiniWob++ benchmark for automatically solving Web tasks. We show that our proposed semantic agent framework enables to (i) warm start agents with semantic background knowledge to faster learn task-dependent optimal behaviour in terms of the expected cumulative reward and (ii) directly reuse the learned behaviour by semantic-based agents that act by automatically derived Notation3 (N3) implication rules.
A Feature-Based Hybrid Medical Image Watermarking Algorithm Based on SURF-DCT

Researching digital watermarking algorithms with better transparency and robustness is essential for protecting the copyright of medical images. Security issues in medical images are increasing day to day with the increase of health data all around the globe. This paper proposed a new algorithm image-based zero-watermarking using SURF-DCT perceptual hashing (Speeded Up Robust Features and Discrete Cosine Transform). Firstly, by using the SURF features of the medical image are taken to perform watermark embedding and extraction. Then by using perceptual hashing and quantization to generate hashing sequences to capture semi-global geometric characteristics. Next, used chaotic maps to encrypt the watermarking and embed it in the medical image after extracting the final features which are used as a hash value. Finally, the correlation coefficient is used to measure the performance of the proposed algorithm with other algorithms against the embedded and extracted watermarking sequences. The experimental results show that the proposed algorithm is robust to many attacks such as clipping, JPEG compression, and filtering. It also has good resistance to attacks such as rotation and noise.
Reflections of Digitalization on Accounting: The Effects of Industry 4.0 on Financial Statements and Financial Ratios

Following the recent Industrial Revolution, Turkey has demonstrated a strong willingness to digitalize their operations and incorporate Industry 4.0 components under firms manufacturing processes. Quickly gaining the support of the Turkish government, the 4.0 movement has flourished and presented researchers with a unique opportunity to compare the pre-post implementation outcomes of the transition. Considering the current international literature on the acknowledged benefits of the Industry 4.0 movement and the inherent cost of incorporating new technology under the organization, managers are naturally expectant that their technological investments results in deserved payouts. Firms have been long motivated in evaluating the potential payback of their investments. However, previous research has faced difficulties when analyzing the relationship between productivity and information technologies. By employing use of the actualized Industry 4.0 benefits under organizations, this Chapter aims to provide an assessment of the implementation outcome of the digitalization process and provides an in-depth understanding of the financial impact of Industry 4.0.
Towards Habit Recognition in Smart Homes for People with Dementia

The demand for smart home technologies that enable ageing in place is rising. Through activity recognition, users’ activities can be monitored. However, for dementia patients, activity recognition alone cannot address the challenges associated with changes in the user’s habits along the disease’s stage transitions. Extending activity recognition to habit recognition enables the capturing of patients’ habits and changes in habits in order to detect anomalies. This paper aims to introduce relevant features for habit recognition solutions, extracted from data, in order to enrich the representation of the user’s habits. This solution is personalisable to meet the specific needs of the patients and generalizable for use in different scenarios. In this way caregivers are better informed on the expected changes of the patient’s habits, which can help to mitigate further deterioration through early treatment and intervention.
EFL Learning Beyond the Wall with MALL

This chapter reports an exploratory study that examines Yemeni college students' perceptions of mobile-assisted language learning (MALL), using their mobile phones, in their informal learning of English as a Foreign Language (EFL). The sample population comprised 150 EFL college students. Data were obtained through a questionnaire based on technology acceptance model (TAM). The findings revealed that EFL college students had positive perceptions towards utilizing MALL for informal EFL learning. Most participants maintained positive perceptions on the two constructs of TAM: perceived usefulness and perceived ease of use. They believed that utilizing MALL furthered their EFL learning. Therefore, policymakers, curriculum designers, and educators are recommended to capitalize on using mobile phones for informal autonomous EFL learning in a way that can serve the objectives of formal EFL learning.
On semi-supervised learning

Major efforts have been made, mostly in the machine learning literature, to construct good predictors combining unlabelled and labelled data. These methods are known as semi-supervised. They deal with the problem of how to take advantage, if possible, of a huge amount of unlabelled data to perform classification in situations where there are few labelled data. This is not always feasible: it depends on the possibility to infer the labels from the unlabelled data distribution. Nevertheless, several algorithms have been proposed recently. In this work, we present a new method that, under almost necessary conditions, attains asymptotically the performance of the best theoretical rule when the size of the unlabelled sample goes to infinity, even if the size of the labelled sample remains fixed. Its performance and computational time are assessed through simulations and in the well- known “Isolet” real data of phonemes, where a strong dependence on the choice of the initial training sample is shown. The main focus of this work is to elucidate when and why semi-supervised learning works in the asymptotic regime described above. The set of necessary assumptions, although reasonable, show that semi-parametric methods only attain consistency for very well-conditioned problems.
Semantic Knowledge Sharing Mechanism Based on Blockchain

In the context of big data, the construction and query of existing knowledge graph are realized by gathering scattered knowledge into a centralized big data platform. However, with the advent of the era of knowledge sharing 3.0, the knowledge value awareness of knowledge owners has generally increased, and it has become increasingly difficult to concentrate knowledge on centralized big data platforms. Blockchain technology is a decentralized and distrusting technology solution proposed in recent years. Combined with blockchain, a decentralized semantic knowledge sharing mechanism based on blockchain is proposed. The decentralized global knowledge graph is constructed by using the inter-node communication mechanism, and the query mode and transaction flow design based on the mechanism are realized. The experimental results show that the semantic knowledge sharing mechanism based on blockchain proposed is feasible, and compared with the traditional centralized knowledge graph, the construction time and query rate are greatly improved.
Extensions of unification modulo ACUI

Abstract The theory ACUI of an associative, commutative, and idempotent binary function symbol + with unit 0 was one of the first equational theories for which the complexity of testing solvability of unification problems was investigated in detail. In this paper, we investigate two extensions of ACUI. On one hand, we consider approximate ACUI-unification, where we use appropriate measures to express how close a substitution is to being a unifier. On the other hand, we extend ACUI-unification to ACUIG-unification, that is, unification in equational theories that are obtained from ACUI by adding a finite set G of ground identities. Finally, we combine the two extensions, that is, consider approximate ACUI-unification. For all cases we are able to determine the exact worst-case complexity of the unification problem.
Automatic detection of canonical image orientation by convolutional neural networks

Automatically detecting the canonical orientation of images based on their visual content is an essential part of many computer vision and image processing pipeline. It allows to disregard meta-data such as EXIF which may not be consistently stored or manipulated across different devices, applications, and file formats. Similar to human perception, convolutional neural networks can exploit implicit object recognition and other useful semantic cues to predict the correct orientation of an image. In this work, we leverage the properties of convolutional neural networks and adapt a pre-trained model to the image orientation detection task. It is shown by extensive evaluation that our method can work well on a range of datasets, including relatively low quality photos generated by a wide range of consumer devices. On public benchmarks, our method compares favourably with the state-of-the-art and achieves accuracy very close to that of humans.
Historical Map Applications and Processing Technologies

Digital map processing has been an interest in the computer science and geographic information science communities since the early 1980s. With the increase of available map scans, a variety of researchers in the natural and social sciences developed a growing interest in using historical maps in their studies. The lack of an understanding of how historical maps can be used in research and the capabilities of map processing technologies creates a significant gap between the wide range of communities that could benefit from the advances in digital map processing technologies and the disciplines in which the technologies are developed. As a result, researchers who intend to use historical maps in their studies still need a significant amount of resources to digitize their maps, while the existing digital map processing technologies are difficult to apply and understand and thus do not look promising. In many cases, existing digital map processing technologies could help facilitate the digitization process, and it just requires additional knowledge to select an appropriate technology given the problem scope (e.g., the number of maps for processing, map conditions, and style varieties). The result is that researchers waste time and resources building and testing various systems that partially duplicate prior work and cannot fully use the potential of existing technology. This chapter presents real-world applications of historical maps and case studies of both semi-automatic and fully automatic approaches for geographic feature extraction from historical maps. These real-world applications illustrate and exemplify various needs and scopes of using historical maps in scientific studies (e.g., processing thousands of historical maps from a map series vs. a few historical maps from various publishers and with different cartographic styles). The two example map processing technologies described help understand current strengths and weaknesses. These examples also illustrate tremendous collaboration opportunities between and beyond the computer science and geographic information science communities to build advanced map processing technologies that are more effective in transforming the scientific studies that use historical maps.
Very High Resolution Image Scene Classification with Capsule Network

Convolutional Neural Network (CNN) has boosted the performance of Very High Resolution (VHR) remote sensing data classification. Moreover, the continuous development of CNN techniques for image scenes description has entered a new challenge. The deep neural network models require a huge number of training samples, which is the main limitation of processing remote sensing data. To overcome this issue, a new method, based on the Capsule neural network for VHR image scenes recognition is proposed in this work. Experiments on the public Aerial Image Dataset (AID) benchmark, containing different areal categories with sub-meter spatial resolution are conducted. The obtained results demonstrate the effectiveness of the proposed method, as compared with the classical CNN model.
Should I (re)Learn or Should I Go(on)?

Continuous, dynamic and short-term learning is an effective learning strategy when operating in dynamic and adversarial environments, where concept drift constantly occurs and attacks rapidly change over time. In an on-line, stream learning model, data arrives as a stream of sequentially ordered samples, and older data is no longer available to revise earlier suboptimal modeling decisions as the fresh data arrives. Stream approaches work in a limited amount of time, and have the advantage to perform predictions at any point in time during the stream. We focus on a particularly challenging problem, that of continually learning detection models capable to recognize cyber-attacks and system intrusions in a highly dynamic and adversarial environment such as the open Internet. We consider adaptive learning algorithms for the analysis of continuously evolving network data streams, using (dynamic) sliding windows -- representing the system memory, to periodically re-learn, automatically adapting to concept drifts in the underlying data. By continuously learning and detecting concept drifts to adapt memory length, we show that adaptive learning algorithms can realize high detection accuracy of evolving network attacks over dynamic network data streams.
The Human Being at the Center of Smart Factories Thanks to Augmented Reality

Nowadays, the interaction of new technologies with humans can offer many advantages. Some of them can be found in the production environment, where the human being retrieved a central role thanks to Augmented Reality (AR). This can augment the human capabilities, compensating the respective limitations and providing benefits in terms of the overall system performances. This paper reports the implementation of an AR system for an industrial grinding process helping the operator, to comlpy working tolerances, with a repeatability comparable to that of an automatic system. An experimental campaign was initially run to identify the best user interface to be used in the process. After that, the interface with the best performance, evaluated on experimental results both from the operators' feedbacks, was used in a grinding operation. The outcome of the process highlighted that, with respect to the standard operation without any aid, the usage AR allowed to achieve better performances and quality.
Chronotopic representations as an effect of individuation: The case of the European migrant crisis

Abstract This article discusses internet memes in their capacity to prompt affective responses on social media in the aftermath of the migrant crisis. The focus is on Facebook pages devoted to geopolitical satire meme-comics known as countryballs and their uptake with regard to the proposed migrant relocation mechanisms. Engagement with internet memes reveals a multilayered complexity behind what is often simplistically portrayed as pro- or anti-migrant sentiment. In order to account for this complexity, the paper combines Gilbert Simondon's theory of individuation with Mikhail Bakhtin's notion of the chronotope currently developed in interactional sociolinguistics along the lines of symbolic interactionism. Finally, this article shows that memes are not a mere product of participatory culture, but rather a powerful instigator of technosocial and often heteroglossic practices that co-organize social life in the new polycentric collectivities appearing on social media. (Chronotope, individuation, internet memes, countryballs, Facebook, identity)*
Memory-Based Evolutionary Algorithms for Nonlinear and Stochastic Programming Problems

In this paper, we target the problems of finding a global minimum of nonlinear and stochastic programming problems. To solve this type of problem, we propose new approaches based on combining direct search methods with Evolution Strategies (ESs) and Scatter Search (SS) metaheuristics approaches. First, we suggest new designs of ESs and SS with a memory-based element called Gene Matrix (GM) to deal with those type of problems. These methods are called Directed Evolution Strategies (DES) and Directed Scatter Search (DSS), respectively, and they are able to search for a global minima. Moreover, a faster convergence can be achieved by accelerating the evolutionary search process using GM, and in the final stage we apply the Nelder-Mead algorithm to find the global minimum from the solutions found so far. Then, the variable-sample method is invoked in the DES and DSS to compose new stochastic programming techniques. Extensive numerical experiments have been applied on some well-known functions to test the performance of the proposed methods.
A Secured Steganography Technique for Hiding Multiple Images in an Image Using Least Significant Bit Algorithm and Arnold Transformation

Security systems are well-liked in several areas wherever technologies are developing day by day. Using secret writing called encryption and data hiding called Steganography information security are often achieved. A traditionally used technique in Steganography is the LSB technique. Hide info is that the art and science of hidden writing, it’s a technique of masking information in an exceedingly different style of data. Additionally, we will send text or a picture disguised by the cover image and change the components of the first to the recipient. A hidden text or image move into the cover image will defend the attacker’s original information. In this we proposed a hybrid technique to secure the images in an image. Here Steganography LSB technique is used to hide the images in an image after that used the scrambling Arnold technique to scramble the cover image and improved the security of the hidden image.
Asymptotically Exact Unweighted Particle Filter for Manifold-Valued Hidden States and Point Process Observations

The filtering of a Markov diffusion process on a manifold from counting process observations leads to `large' changes in the conditional distribution upon an observed event, corresponding to a multiplication of the density by the intensity function of the observation process. If that distribution is represented by unweighted samples or particles, they need to be jointly transformed such that they sample from the modified distribution. In previous work, this transformation has been approximated by a translation of all the particles by a common vector. However, such an operation is ill-defined on a manifold, and on a vector space, a constant gain can lead to a wrong estimate of the uncertainty over the hidden state. Here, taking inspiration from the feedback particle filter (FPF), we derive an asymptotically exact filter (called ppFPF) for point process observations, whose particles evolve according to intrinsic (i.e., parametrization-invariant) dynamics that are composed of the dynamics of the hidden state plus additional control terms. While not sharing the gain-times-error structure of the FPF, the optimal control terms are expressed as solutions to partial differential equations analogous to the weighted Poisson equation for the gain of the FPF. The proposed filter can therefore make use of existing approximation algorithms for solutions of weighted Poisson equations.