Reviewerly/exHarmony huggingface dataset:
Secure Communication for Multiquadrotor Networks Using Ethereum Blockchain

Ethereum blockchain is a powerful, open-source technology for creating decentralized and secure information sharing systems. The main contribution of this article is the experimental validation of an Ethereum blockchain-based software and hardware architecture that enables secure communication for multiple small unmanned aerial vehicles (sUAVs). The experiments involved three DJI M100 quadrotors that shared images captured during flight based on smart contracts created using Ethereum’s Turing complete programming language. The smart contract was designed so that only the intended recipient sUAV could access a specific image. The effect of image size, difficulty level, and consensus algorithms on image transfer times during flight are noted and point to the feasibility of this system in practical missions. The effects of wireless network disruptions on the Ethereum network are documented. The fully documented smart contract code is open sourced to assist readers in quick prototyping. As efforts for decentralization and security of multirobot systems continue to grow, the system architecture and implementation detailed here may serve as a guide for future research.
Robust fitting of parallax-aware mixtures for path guiding

Effective local light transport guiding demands for high quality guiding information, i.e., a precise representation of the directional incident radiance distribution at every point inside the scene. We introduce a parallax-aware distribution model based on parametric mixtures. By parallax-aware warping of the distribution, the local approximation of the 5D radiance field remains valid and precise across large spatial regions, even for close-by contributors. Our robust optimization scheme fits parametric mixtures to radiance samples collected in previous rendering passes. Robustness is achieved by splitting and merging of components refining the mixture. These splitting and merging decisions minimize and bound the expected variance of the local radiance estimator. In addition, we extend the fitting scheme to a robust, iterative update method, which allows for incremental training of our model using smaller sample batches. This results in more frequent training updates and, at the same time, significantly reduces the required sample memory footprint. The parametric representation of our model allows for the application of advanced importance sampling methods such as radiance-based, cosine-aware, and even product importance sampling. Our method further smoothly integrates next-event estimation (NEE) into path guiding, avoiding importance sampling of contributions better covered by NEE. The proposed robust fitting and update scheme, in combination with the parallax-aware representation, results in faster learning and lower variance compared to state-of-the-art path guiding approaches.
Based on IoT Healthcare Application for Medical Data Authentication: Towards A New Secure Framework Using Steganography

Abstract With increasing transmission of sensitive information over the dispersed IoTs, security of sensitive patient’s contents is becoming more challenging and has been enthusiastic area of research since last decades. Evolution in the concealment of data was reflected in the medical field specifically on medical images. Hide information technology in the image is called steganography. The objective of this study is the preservation of privacy and confidentiality of data in uncertain surroundings during multimedia exchange joining two IoT hops. For attacker hindrance and, provision of data confidentiality, a resilient multilevel security perspective depending on information hiding and cryptography is suggested. The existing schemes have limitations related to the equilibrium trade-off amid two variables (medical image quality and security). In addition, the direct embedment of the secret data into the images and further subtraction of an encrypted data from it often enables the intruders to easily detect and extract the hidden information. Based on these factors, we proposed a multilevel security based on 3 th random iterations with chosen a procedure was implemented using Henon function to stop against cybercrimes challenges. The patient information is going through the preparation stage (different steps) before the embedding algorithm in order to increase the security. Superior results achieved with this study in term of imperceptibility and security the reason is to choose the right method in the right place. Satisfying results, gained when benchmarking our results with existing one in literature through the same criteria.
Is a cover letter still needed? Using LIWC to predict application success

Abstract This study investigated whether word categories of LIWC (Linguistic Inquiry and Word Count) are able to predict application success. To this end, 581 cover letters, CVs and complete application documents were analysed. Based on previous research, successful candidates, who receive a job offer, were expected to have used a more categorical, complex, and less self‐centred language. Conversely, rejected applications were expected to have been written in a dynamic style, linguistically simpler, more concerned with hedonistic issues and focused on the day‐to‐day lives. Overall, existing models could only be partially applied. Both the cover letter and the CV were found to contain predictive information regarding application success, which is noteworthy given the distinct standardization of application documents.
Opening the Deep Pandora Box: Explainable Traffic Classification

Fostered by the tremendous success in the image recognition field, recently there has been a strong push for the adoption of Convolutional Neural Networks (CNN) in networks, especially at the edge, assisted by low-power hardware equipment (known as “tensor processing units”) for the acceleration of CNN-related computations. The availability of such hardware has reignited the interest for traffic classification approaches that are based on Deep Learning. However, unlike tree-based approaches that are easy to interpret, CNNs are in essence represented by a large number of weights, whose interpretation is particularly obscure for the human operators. Since human operators will need to deal, troubleshoot, and maintain these automatically learned models, that will replace the more easily human-readable heuristic rules of DPI classification engine, there is a clear need to open the “deep pandora box”, and make it easily accessible for network domain experts. In this demonstration, we shed light in the inference process of a commercial-grade classification engine dealing with hundreds of classes, enriching the classification workflow with tools to enable better understanding of the inner mechanics of both the traffic and the models.
New Techniques for Traitor Tracing: Size $$N^{1/3}$$ and More from Pairings

The best existing pairing-based traitor tracing schemes have $$O(\sqrt{N})$$ -sized parameters, which has stood since 2006. This intuitively seems to be consistent with the fact that pairings allow for degree-2 computations, yielding a quadratic compression. In this work, we show that this intuition is false by building a traitor tracing scheme from pairings with $$O(\root 3 \of {N})$$ -sized parameters. We additionally give schemes with a variety of parameter size trade-offs, including a scheme with constant-size ciphertexts and public keys (but linear-sized secret keys). We obtain our schemes by developing a number of new traitor tracing techniques, giving the first significant parameter improvements in pairings-based traitor tracing in over a decade.
Energy cost minimization for sustainable cloud computing using option pricing

One of the key requirements of a sustainable city is the efficiency of the underlying technologies implemented for the provision of services to its residents. Cloud Computing is one such technology that offers a variety of applications support for sustainable cities such as infrastructure for processing of big data and provision of real time services to end users. However, power management of cloud computing infrastructure (data centers) becomes a challenging issue for Cloud Service Providers (CSPs), having negative financial, environmental and sustainability implications. To process the user requests (incoming workload), the data centers (DCs) incur huge energy (electricity) bills. Current studies indicate the workload allocation strategies among geographical distributed DCs mostly focus on using renewable energy and cheaper rates of electricity to reduce the overall energy costs. In this paper, we explore the energy cost minimization problem for geo-distributed DCs considering call option in electricity derivative market under time-varying system dynamics. A call option is an agreement between CSPs and electricity supplier, which gives the rights (not obligation) to option holder to purchase a specific amount of electricity over a time period at a predefined fixed price. To minimize the energy cost, we propose an online algorithm for interactive workload distribution called option pricing based geographical load balancing (OptionGLB). Experimental evaluation based on real-world data indicates the efficacy of OptionGLB over current workload allocation strategies.
Estimating Quantum Entropy

The entropy of a quantum system is a measure of its randomness, and has applications in measuring quantum entanglement. We study the problem of estimating the von Neumann entropy, S(ρ), and Rényi entropy, S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">α</sub> (ρ) of an unknown mixed quantum state ρ in d dimensions, given access to independent copies of ρ. We provide algorithms with copy complexity O(d <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2/α</sup> ) for estimating Sα(ρ) for α <; 1, and copy complexity O(d <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) for estimating S(ρ), and S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">α</sub> (ρ) for non-integral α > 1. These bounds are at least quadratic in d, which is the order dependence on the number of copies required for estimating the entire state ρ. For integral α > 1, on the other hand, we provide an algorithm for estimating S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">α</sub> (ρ) with a sub-quadratic copy complexity of O(d <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2-2/α</sup> ), and we show the optimality of the algorithms by providing a matching lower bound.
Active Learning-based Classification in Automated Connected Vehicles

Machine learning has emerged as a promising paradigm for enabling connected, automated vehicles to autonomously cruise the streets and react to unexpected situations. A key challenge, however, is to collect and select real-time and reliable information for the correct classification of unexpected, and often uncommon, events that may happen on the road. Indeed, the data generated by vehicles, or received from neighboring vehicles, may be affected by errors or have different levels of resolution and freshness. To tackle this challenge, we propose an active learning framework that, leveraging the information collected through onboard sensors as well as received from other vehicles, effectively deals with scarce and noisy data. In particular, given the available information, our solution selects the data to add to the training set by trading off between two essential features: quality and diversity. The results, obtained using realworld data sets, show that our method significantly outperforms state-of-the-art solutions, providing high classification accuracy at the cost of a limited bandwidth requirement for the data exchange between vehicles.
Images super-resolution by optimal deep AlexNet architecture for medical application: A novel DOCALN1

In the last decade, numerous researches have been focused on Image Super-Resolution (SR); this recreation or improvement model is vital in different research areas. Recently, deep learning algorithm finds useful to advance in the resolution of the medical output. Here, we devise a novel Deep Convolutional Network model along with the optimal learning rate of the Rectified Linear Unit (ReLU) intended for Medical Image Super-Resolution (MISR). For getting the optimal values of Deep Learning AlexNet structure, Modified Crow Search (MCS) is utilized, which is mainly depends on the behavior of crow sets. The chosen Alexnet lacks in a sort of suitable supervision for upgrading execution of the proposed model that effectively aims to overfit. The proposed design, i.e., MISR, named Deep Optimal Convolutional AlexNet (DOCALN), derives the optimal values of learning rates of the ReLU activation function. Based on this optimal deep learning structure, the Low Resolution (LR) medical images can be applied. Experimentation results of our proposed model are compared with variants of Convolution Neural Networks (CNN) concerning different measures such as image quality assessment, SR efficiency analysis, and execution time.
Nonlinear color triads for approximation, learning and direct manipulation of color distributions

We present nonlinear color triads, an extension of color gradients able to approximate a variety of natural color distributions that have no standard interactive representation. We derive a method to fit this compact parametric representation to existing images and show its power for tasks such as image editing and compression. Our color triad formulation can also be included in standard deep learning architectures, facilitating further research.
Delineation of geochemical haloes using the developed zonality index model by multivariate and fractal analysis in the Cu–Mo porphyry deposits

Main aim of this study is to propose a developed zonality index for Cu–Mo porphyry deposits based on staged factor analysis with Number-Size (N–S) fractal model. This method was used in an Iranian porphyry system as named Serenu (SE Iran). The developed zonality index was a ratio between factors related to major and minor ore elements based on in-situ soil samples. In this study, two factors were extracted after three stages of factor analysis including F1-3 (Pb, Zn, Au, Ag, Mn, Cd, As) and F2-3 (Cu–Mo). Based on these factors, F1−3F2−3is determined as a developed zonality index. Furthermore, conventional zonality index was calculated on the basis of(Pb×Zn)(Cu×Mo). Then, these zonality indexes were classified by the N–S fractal modeling. Results obtained by the fractal modeling indicates that the main anomalies for the developed zonality index is correlated with plutonic units, mineralized rock samples and exploratory boreholes especially in the central and SE parts of the studied area. This methodology can be utilized for other types of ore mineralization.
Using remote sensing to calculate floating photovoltaic technical potential of a dam’s surface

A dam with a hydroelectric power plant (HEPP) prevents flooding while generating electricity and providing controlled irrigation of agricultural land. An open dam surface causes a substantial loss in water resources over the course of a year due to evaporation. In this paper, the authors propose to occupy the idle dam area with a floating photovoltaic (FPV) solar power plant (SPP) to generate electrical energy and to conserve water by minimizing evaporation. Since the shoreline of a dam used for agricultural irrigation continually changes, the most critical challenge in installing a SPP is to determine the suitable area to be covered with FPV panels. In this study, the shoreline changes of the Demirköprü Dam in Manisa, Turkey, were monitored over 20 years from Landsat and Sentinel satellite images using the supervised classification in the Google Earth Engine. The minimum surface area of the dam was found to be 1,562.45 ha. Installing a 2.03 GWp FPV SPP horizontally on this surface and obtaining 3,328.33 GWh annual energy is feasible. Moreover, the FPV panels can prevent 28,231,026.90 m3 of water from evaporating. Approximately 7.82% of the water used for electricity production in 2019 can be recovered with the installation of this SPP.
Collaborative Cyber Attack Defense in SDN Networks using Blockchain Technology

The legacy security defense mechanisms cannot resist where emerging sophisticated threats such as zero-day and malware campaigns have profoundly changed the dimensions of cyber-attacks. Recent studies indicate that cyber threat intelligence plays a crucial role in implementing proactive defense operations. It provides a knowledge-sharing platform that not only increases security awareness and readiness but also enables the collaborative defense to diminish the effectiveness of potential attacks. In this paper, we propose a secure distributed model to facilitate cyber threat intelligence sharing among diverse participants. The proposed model uses blockchain technology to assure tamper-proof record-keeping and smart contracts to guarantee immutable logic. We use an open-source permissioned blockchain platform, Hyperledger Fabric, to implement the blockchain application. We also utilize the flexibility and management capabilities of Software-Defined Networking to be integrated with the proposed sharing platform to enhance defense perspectives against threats in the system. In the end, collaborative DDoS attack mitigation is taken as a case study to demonstrate our approach.
Digital forensic tools: Recent advances and enhancing the status quo

Publications in the digital forensics domain frequently come with tools – a small piece of functional software. These tools are often released to the public for others to reproduce results or use them for their own purposes. However, there has been no study on the tools to understand better what is available and what is missing. For this paper we analyzed almost 800 articles from pertinent venues from 2014 to 2019 to answer the following three questions (1) what tools (i.e., in which domains of digital forensics): have been released; (2) are they still available, maintained, and documented; and (3) are there possibilities to enhance the status quo? We found 62 different tools which we categorized according to digital forensics subfields. Only 33 of these tools were found to be publicly available, the majority of these were not maintained after development. In order to enhance the status quo, one recommendation is a centralized repository specifically for tested tools. This will require tool researchers (developers) to spend more time on code documentation and preferably develop plugins instead of stand-alone tools.
VIStory: interactive storyboard for exploring visual information in scientific publications

Many visual analytics have been developed for examining scientific publications comprising wealthy data such as authors and citations. The studies provide unprecedented insights on a variety of applications, e.g., literature review and collaboration analysis. However, visual information (e.g., figures) that is widely employed for storytelling and methods description are often neglected. We present VIStory, an interactive storyboard for exploring visual information in scientific publications. We harvest a new dataset of a large corpora of figures, using an automatic figure extraction method. Each figure contains various attributes such as dominant color and width/height ratio, together with faceted metadata of the publication including venues, authors, and keywords. To depict these information, we develop an intuitive interface consisting of three components: (1) Faceted View enables efficient query by publication metadata, benefiting from a nested table structure, (2) Storyboard View arranges paper rings-a well-designed glyph for depicting figure attributes, in a themeriver layout to reveal temporal trends, and (3) Endgame View presents a highlighted figure together with the publication metadata. We illustrate the applicability of VIStory with case studies on two datasets, i.e., 10-year IEEE VIS publications, and publications by a research team at CVPR, ICCV, and ECCV conferences. Quantitative and qualitative results from a formal user study demonstrate the efficiency of VIStory in exploring visual information in scientific publications.
Listen, Look, and Find the One

Person search with one portrait, which attempts to search the targets in arbitrary scenes using one portrait image at a time, is an essential yet unexplored problem in the multimedia field. Existing approaches, which predominantly depend on the visual information of persons, cannot solve problems when there are variations in the person’s appearance caused by complex environments and changes in pose, makeup, and clothing. In contrast to existing methods, in this article, we propose an associative multimodality index for person search with face, body, and voice information. In the offline stage, an associative network is proposed to learn the relationships among face, body, and voice information. It can adaptively estimate the weights of each embedding to construct an appropriate representation. The multimodality index can be built by using these representations, which exploit the face and voice as long-term keys and the body appearance as a short-term connection. In the online stage, through the multimodality association in the index, we can retrieve all targets depending only on the facial features of the query portrait. Furthermore, to evaluate our multimodality search framework and facilitate related research, we construct the Cast Search in Movies with Voice (CSM-V) dataset, a large-scale benchmark that contains 127K annotated voices corresponding to tracklets from 192 movies. According to extensive experiments on the CSM-V dataset, the proposed multimodality person search framework outperforms the state-of-the-art methods.
A Routing Framework for Heterogeneous Multi-Robot Teams in Exploration Tasks

This letter proposes a routing framework for heterogeneous multi-robot teams in exploration tasks. The proposed framework deals with a combinatorial optimization problem, and provides a new solving algorithm, for Generalized Team Orienteering Problem (GTOP). In this letter, a route optimization problem is formulated for a heterogeneous multi-robot system. A novel problem solver is also proposed based on self-organizing map. The proposed framework has a strong advantage in its scalability because the processing time is independent from the number of robots, and the heterogeneity of the team. The validity of the proposed framework is evaluated in the exploration, and mapping tasks by heterogeneous robot team with overlapping abilities. The simulation results show the effectiveness of the proposed framework, and how it outperforms the conventional greedy exploration scheme.
On Modeling and Simulation of Resource Allocation Policies in Cloud Computing Using Colored Petri Nets

The Petri net (PN) formalism is a suitable tool for modeling parallel systems due to its basic characteristics, such as synchronization. The extension of PN, the Colored Petri Nets (CPN) allows the incorporation of more details of the real system into the model (for example, contention for shared resources). The CPNs have been widely used in a variety of fields to produce suitable models. One of their biggest strengths is that their overall philosophy is quite similar to the philosophy of the object-oriented paradigm. In this regard, the CPN models can be used to implement simulators in a rather straightforward way. In this paper, the CPN framework is employed to implement a new resource allocation simulator, which is used to verify the performance of our previous work, where we proposed a fair resource allocation scheme with flow control and maximum utilization of the system’s resources.