Reviewerly/exHarmony huggingface dataset:
Analytical Study of Augmented Reality Simulation System Application on Paintings

Augmented reality technology is a revolution of the 21st-century on the other side paintings are a traditional tool of expression. Representation of painting through technology is innovative, effective, and exciting to watch. The use of augmented reality is rising in every sector, and it has changed the perception of paintings in the last few years. As we know paintings are the most beautiful and famous way to express an artist’s feelings and thoughts on canvas, and this time art and technology collaborative study gave an extraordinary and unique result. This research paper is about the augmented reality simulation system application on paintings and its communicating presentation. It is an interactive installation art due to the involvement of spectators. Augmented Reality paintings are demanding in the international art market, as artists accepted that augmented reality interferes in fine arts territory. This paper is about the process and analytical study of augmented reality simulations on paintings with conceptual thinking and its deliberation as a new media art in the international art market.
Analysis of IoT Device Network Traffic: Thinking Toward Machine Learning

The proliferation and diversity of Internet of things (IoT) devices increase IoT system risks. This open many challenging area for research like identifying authorized and unauthorized IoT devices, classifying events of devices, detecting network traffic anomalies generated by such devices, etc. To do so, machine and deep learning algorithms should be applied after extracted a set of features from the associated device’s traffic. The identification accuracy as well as the computational time are both very important factors, especially with the limited resources system. These considerations are firstly dependent on which features list is taken. So, the IoT network traffic analysis is the more important and difficult step in IoT device identification. In this paper, the traffic of six IoT home devices is analyzed using wireshark network protocol analyzer. A set of features and protocols are studied to gain insight into the important ones that can be utilized for creating a device fingerprint for identification purposes in the IoT environment. The result of the analytical study was that some features are unsuitable to be certified in the case of the testbed devices' traffic, while others are expressive features that can be used to identify devices according to their manufacturers.
Recommended to you: Generierte Empfehlungen für Konzerte. Potenziale und Herausforderungen bei der Anwendung von Recommender Systems im Kontext von Konzerten

Several studies have shown the importance of personal recommendations in attracting attention to cultural events. Nowadays, not only friends can make suitable recommendations, but also algorithms based on the behavior of Internet users. This article examines different types of recommendations with their benefits and limits in the context of marketing. For a better understanding of the opportunities of such technologies the special features of concert events are described, which leads to the applications, where such generated recommendations are used for concerts. A quantitative study is conducted to find out what role generated recommendations currently play among young concert goers compared to personal recommendations. Since the decision to buy concert-tickets is also related to listening habits, another focus is on the question to what extent generated music recommendations in streaming services lead to later concert visits.
Development and Evaluation of a Tangible Interaction Concept for Assembly Workstations

In the development and research of IT-supported systems, the user is increasingly in the center of attention. Therefore, we investigated the approach of how beneficial it would be to use tangibles (physical objects) as an interaction option for the user at an assemble workstation. For this purpose, we designed an augmented reality concept for the use of tangibles at a manual assembly workstation and evaluated it with an eye tracking study. Through the study, we found that the participants were very comfortable with the tangible interaction concept, learned very quickly, and found it very easy to use. Further, we present how it is possible to improve assistance systems, such as pick-by-light systems, with the use of augmented reality and tangibles. For this, we show that the use of tangibles offers many advantages like training new employees fast at an assembly workstation.
Cloud Computing Services and Microsoft Azure. Why Microsoft Azure?

CONCEPT: Although Amazon Web Services has the top place in the market as a cloud platform, it is not a top choice for each client to any degree. Microsoft Azure has know-how in backup programming as an assistant to IoT and retail organizations, and the outline has different devices for setting up, overseeing, and making sensors. Likewise, it gives valuable properties for constant information examination, handling of knowledge, and AI. OBJECTIVES After a brief introduction to The Microsoft Azure platform, the primary objective of this paper is to discuss Microsoft Azure and its benefits briefly in comparison to other cloud services. METHODS A review of all relevant sources known to the authors was carried out. RESULTS Microsoft Azure offers a set of capabilities and features that surpass that of other cloud platforms. Important aspects where Azure has been found to score over other cloud platforms were the capabilities of PaaS, compatibility of the net, the security it offers, and seamless cloud connectivity hybrid solutions. CONCLUSIONS Advantages of Azure over other cloud computing services were noted in this study. Although Amazon Web Services is a leading platform, Microsoft Azure could be a better option because it provides a consistent platform that facilitates easy movement between the public cloud and on-premises. It also has other benefits like Unmatched Hybrid capabilities, Easy learning, Cost-Efficient, Identity & Access Management, Security Offerings, Scalability, and Ductility.
Refined UNet Lite: End-to-End Lightweight Network for Edge-precise Cloud Detection

Inevitable cloud regions dramatically degrade the visibility of remote sensing images, and resulting cloud detection is one of the essential prerequisites for applications of remote sensing images. In Refined UNets, we explored solutions to the edge-precise cloud and shadow detection for remote sensing images of the Landsat 8 OLI set; in particular, v2 and v3 attempted to provide end-to-end inference prototypes. Refined UNets exploited iterative CRF inference to refine edges of cloud and shadow regions, making it difficult to train the entire model in a joint way. We instead turn to investigate an entirely differentiable model to satisfy the requirement of end-to-end training. To this end, we present an efficient and lightweight model to identify cloud regions, referred to as Refined UNet lite, which is able to facilitate end-to-end training and inference and partially contributes to edge-precise cloud detection. Specifically, the UNet backbone locates cloud regions coarsely and the subsequent guided-filter layer refines their finegrained edges. All operators within our model are fully differentiable, leading to an end-to-end trainable model instead of a partially trainable one. In addition, the practical time consumptions significantly decrease due to the lightweight architecture of our model. The comprehensive experiments confirm our contributions to efficient edge-precise cloud detection. The implementation of Refined UNet lite is currently available at https://github.com/92xianshen/refined-unet-lite.
Demonstration-Based Proximal Policy Optimization with Action Guidance

Exploration remains a major challenge in the field of Reinforcement Learning (RL), especially in an environment in which there are sparse reward signals. Recently, it has been proved that some learning methods using expert demonstrations and enhanced exploration signals from the environment can effectively overcome exploration difficulty. However, they require massive high-quality expert data. To address this, this paper develops a novel Proximal Policy Optimization from Demonstration (PPOfD) method using the demonstrations combined with agent’s subsequent successful trajectories for the benefit of action guidance. Subsequently, an adaptive parameter is designed to integrate this idea with the proximal policy optimization more efficiently. Besides, we illustrate how PPOfD guides the formation of implicit rewards to bring demonstrable benefits for policy improvement. Finally, the performance of the proposed method is evaluated in a series of popular sparse-reward tasks. Experimental results show the proposed PPOfD surpasses three state-of-the-art baselines even when the demonstrations are few and imperfect.
How Technology Devices Can Help or Harm Vulnerable Communities in Technocene. Issues for Designers, Architects, and Policy Makers

This paper explores the important role of technology development in contemporary age for vulnerable communities. Combining the widespread concept of Anthropocene with the philosophical position which considers technology as the environment of humanity, these research aims to understand the challenges for design, architecture, and politics and to get a comprehension how technology can help or harm vulnerable communities in the next decades. The research is based on a deep analysis of the existing literature to highlight, from the perspective of urban-architectural design, the potential risks (technology illiteracy, data production, and exclusion) and benefits (reduction of distances, self-production, transportation, monitoring, and engagement) of technological development for vulnerable communities. Aspects necessary for equitable and sustainable development emerge: the involvement of the vulnerable population in the adoption of technology, the adoption of effective public policies, investments in infrastructure, educational efforts, and attention to generate a non-exclusive market. These needs are added to a scenario in which the designer engaged in activities in vulnerable communities must face other contemporary emergencies (environmental, social crises, etc.) and adopt new methodologies of involving the population (self-construction, participatory design, etc.). This research therefore highlights issues for designers, architects, and policy makers generated by technological development and its possible impacts on vulnerable communities.
Recent Trends and Techniques of CBIR to Enhance Retrieval Performance

The vast growth in social media platforms such as Twitter, Instagram, Whatsapp, Facebook, etc. leads to the uploading of billions of images on the web. Content-Based Image Retrieval (CBIR) is essential to improve the performance of the data search. Computer vision research community facing research challenges related to the retrieval of relevant images from large databases. Most of the current search engines use text-based search whose performance highly depends on text Annotation and metadata of the images. In this paper, we aim to present an extensive survey of recent work carried out on CBIR based on various attributes. It also covers the comprehensive review of the recent deep learning based CBIR techniques. Further, it focuses on challenges, constraints, and future scope for further research.
Hard and Soft Labeling for Hebrew Paleography: A Case Study

Paleography studies the writing styles of manuscripts and recognizes different styles and modes of scripts. We explore the applicability of hard and soft-labeling for training deep-learning models to classify Hebrew scripts. In contrast to the hard-labeling scheme, where each document image has one label representing its class, the soft-labeling approach labels an image by a label vector. Each element of the vector is the similarity of the document image to a certain regional writing style or graphical mode. In addition, we introduce a dataset of medieval Hebrew manuscripts that provides complete coverage of major Hebrew writing styles and modes. A Hebrew paleography expert manually annotated the ground truth for soft-labeling. We compare the applicability of soft and hard-labeling approaches on the presented dataset, analyze, and discuss the findings.
K-PPE: A Variant Phasmatodea Population Evolution Algorithm for Global Optimization

Pan, Jeng-Shyang Hu, Pei Chu, Shu-Chuan Snášel, Václav Phasmatodea population evolution (PPE) algorithm is a new meta-heuristic, which has presented great performance in engineering optimization problems. A novel version, called K-PPE, is proposed to advance PPE. K-PPE improves the method of generating K optimal solutions and adopts dynamic value of K to promote convergence rate and local exploration. CEC2013 verifies that the performance of K-PPE is better than PPE, particle swarm optimization (PSO) and gray wolf optimizer (GWO). In the end, it is successfully used in multilevel image segmentation.
Augmented Reality in Art: Aesthetics and Material for Expression

Cinematic Apparatus Theory of the 1970s sets the stage for cinematic deconstruction in avant-garde film art. The material and production elements repressed in the normal ideological apparatus became the arena for new expression. Cinema, through its acceleration of mechanization and sequence, became the essential medium of its era; augmented reality accelerates the electric video image and holds promise to be the essential medium of our new era. This essay excavates and diagrams the AR apparatus to search out the repressed in viewers’ perception and point a way forward toward an avant-garde augmented reality art.
Multi-track Transfer Reinforcement Learning for Power Consumption Management of Building Multi-type Air-Conditioners

In this paper, we apply reinforcement learning to the power management control of building multi-type air-conditioners. In general, reinforcement learning requires several tens of thousands of training episodes before the control performance reaches a practical level. Therefore, applying it directly to air-conditioning control in 10-min intervals would require unrealistic training days as several years. We attempted to shorten the learning period by learning in advance on a virtual building that emulates the dynamic characteristics of an actual building. Since it is difficult to create exactly the same air-conditioning environment of the actual building, we propose a method to select the closest one from several virtual buildings based on the differences of immediate reward.
Substring Searchable Symmetric Encryption Based on an Improved DAWG

A searchable symmetric encryption (SSE) scheme is a method that searches encrypted data without decrypting it. In this paper, we address the substring search problem such that for a set D of documents and a pattern p, we find all occurrences of p in D. Here, a document and a pattern are defined as a string. A directed acyclic word graph (DAWG), which is a deterministic finite automaton, is known for solving a substring search problem on a plaintext. We improve a DAWG so that all transitions of a DAWG have distinct symbols. Besides, we present a space-efficient and secure substring SSE scheme using an improved DAWG. The proposed substring SSE scheme consists of an index with a simple structure, and the size is O(n) for the total size n of documents.
Automated TTC Image-Based Analysis of Mouse Brain Lesions

Small animals stroke models have widely been used to study the mechanisms of ischemic brain damage in controllable experimental settings. The evaluation of stroke lesions mainly relies on visual inspection of tissue samples collected after brain sectioning, slice staining and scanning, a procedure that is highly subjective and prone to human error. In this study we developed a machine-learning based methodology for automatic segmentation of lesions in mouse brain tissue samples, stained with Triphenyltetrazolium chloride (2% TTC). Our approach relies on the creation of a statistical mouse brain atlas of healthy TTC slices that was lacking in the literature. For this purpose we applied tissue clustering and Markov Random Fields (MRF) for brain tissue detection followed by deformable image registration for spatial normalization. The obtained statistical atlas is then exploited by outlier detection techniques and Random Forest classification to extract lesion probability maps in new slices. The good agreement between our segmentation results and expert-based lesion delineation on 12 mouse brains highlights the potential of the proposed approach to automate stroke volumetry analysis, thereby contributing to increased translational capacity of experimental stroke.
A Braitenberg Path Planning Strategy for E-puck Mobile Robot in Simulation and Real-Time Environments

An efficient method for path planning of mobile robots is being proposed in this paper. The robot is tested to navigate through different types of obstacle to reach the final goal point. To detect and avoid obstacles a Braitenberg vehicle method is used. The implementation of this method can be performed without using much expensive equipments or any kind of complex structures. For the mobile robot, the simulation has been performed using E-puck robot. It is very important for the robot to have the ability to locate and approach its final destination point. Till date there are many different types of techniques available for path planning. In this paper Braitenberg vehicle is being studied and implemented by considering the coordinates of the robot and the destination point. The behaviour of the robot has been tested in a simulated environment using the software Webots and the results are further validated with real-time experiment by using the same environments to check the robot’s efficiency. During Simulation and real-time experimentation process few complications were identified and it has been successfully resolved.
Code Reviewer Recommendation Systems: Past and Present

Context: Selecting a source code reviewer for a change to a software project is an important aspect of software development. As this decision is frequent and depends on several factors, code review recommender systems (CRRS) have been developed to reduce the cognitive load of software maintenance personnel and speed up the decision-making process. Objective: The work provides a step towards understanding existing solutions for CRRS, such as the factors to be considered when building them. Our goal is to improve researchers understanding of important features of CRRSs so that existing and future systems can better support the information needs of software maintenance personnel. Method: To understand the important and missing features of current CRRS, both a systematic literature review study (SLRS) and a survey of software development project members were conducted. Results: We categorized the selected papers into two categories: based on the data type used to make recommendations and the kinds of projects used for evaluation. The survey helped us understand the features missing in CRRSs and observe some trends and patterns and make recommendations for future CRRS. Conclusion:Â With this literature review study we were able to find which features and factors are important for a CRRS and based o the survey conducted by us we found which of these features are important in a CRRS for software project members and what improvements can be done in the existing CRRSs.
Icd: A New Interpretable Cognitive Diagnosis Model for Intelligent Tutor Systems

Cognitive diagnosis plays a crucial role in Intelligent Tutor Systems, which aims to diagnose learners' cognitive states according to learners' observable records, such as exercise records. However, this paper observes that the existing cognitive diagnosis models still have rooms for performance improvements: (1) they ignore the interaction between knowledge concepts; (2) they ignore the quantitative relation between exercises and concepts. In order to solve the aforementioned problems, this paper proposes a new interpretable cognitive diagnosis model named ICD based on a novel neural network designed by us, which can conduct answer records with different lengths. The neural network can fully utilize the interaction among concepts, the quantitative relation between exercises and concepts, and considers bias replacement by slip and guess, which makes the ICD more interpretable. The paper has conducted extensive experiments on four real publicity datasets, and the results show that both the performances and the interpretability of ICD are better than the latest state-of-the-art CDMs such as RCD, NCDM, and CDGK, and the classical CDMs such as DINA and MIRT.
Secure Watermarking to Protect Colour Images on Social Media from Misuse

Nowadays the use of electronic media is increasing very rapidly. Especially during the pandemic situation of COVID-19, it has been increased very fast. During lockdown people shared their images on social media, IT industries are working online and people are sharing data to each other in online mode, i.e. in multimedia mode. Multimedia data contains text, video, audio and software, etc. Social media is one of the big platforms to share their contents in multimedia form. Every person is sharing his/her data without knowing about intruders. Intruders can misuse the data posted on web. Social media is the biggest platform for scammers. Hence, the security of digital content is the major issue before us. Various researchers are doing work in this area. Watermarking technique is the most usable protecting techniques from misuse of digital information. The proposed technique in the paper using secure watermarking is useful to protect colour images using unique ID Aadhar number, Discrete Wavelet Transform and Singular Value Decomposition. The experimental results show that this technique is robust and can be used to claim the authenticity during any legal issue.
Topo-Cut: A 3d Topological-Based Approach to Correct Watershed Under-Segmentation of Granular Materials with Nearly-

We propose a new 3D approach - the TOPO-CUT algorithm - to treat the problem of under-segmentation which can potentially occur when the watershed approach is used to perform the digital separation of objects. This is an important issue to be addressed, especially in the case of granular material, where the contact between objects is inevitable and their separation is necessary to perform local quantitative or numerical analysis during e.g. flowing conditions. The approach adopts topological-based algorithms, such as curvilinear skeletonization and hole closing to perform the separation of at least 2 objects in contact. It supposes that the digital volumetric image of the granular material is composed of nearly-convex objects. The new approach is tested on real 3D images of two types of grain mixtures made of sorghum and rice. The method outperforms the iterative call of the markerbased watershed algorithm and allows up to 13% of the possible problematic objects to be successfully separated in a limited number of iterations.