nils-herrmann/clean_scirepeval huggingface dataset:
A Closed-Form Solution to Photorealistic Image Stylization

Photorealistic image stylization concerns transferring style of a reference photo to a content photo with the constraint that the stylized photo should remain photorealistic. While several photorealistic image stylization methods exist, they tend to generate spatially inconsistent stylizations with noticeable artifacts. In this paper, we propose a method to address these issues. The proposed method consists of a stylization step and a smoothing step. While the stylization step transfers the style of the reference photo to the content photo, the smoothing step ensures spatially consistent stylizations. Each of the steps has a closedform solution and can be computed efficiently. We conduct extensive experimental validations. The results show that the proposed method generates photorealistic stylization outputs that are more preferred by human subjects as compared to those by the competing methods while running much faster. Source code and additional results are available at https://github.com/NVIDIA/FastPhotoStyle.
A study on encryption using three-dimensional cellular automata

With the exponential growth of information being transmitted as a result of various networks, the issues related to providing security to transmit information have considerably increased. Mathematical models were proposed to consolidate the data being transmitted and to protect the same from being tampered with. Work was carried out on the application of 1D and 2D cellular automata (CA) rules for data encryption and decryption in cryptography. A lot more work needs to be done to develop suitable algorithms and 3D CA rules for encryption and description of 3D chaotic information systems. Suitable coding for the algorithms are developed and the results are evaluated for the performance of the algorithms. Here 3D cellular automata encryption and decryption algorithms are used to provide security of data by arranging plain texts and images into layers of cellular automata by using the cellular automata neighbourhood system. This has resulted in highest order of security for transmitted data.
Deep Hybrid Models : Bridging Discriminative and Generative Approaches

A New Framework For Hybrid Models By Coupling Latent Variables 1 User specifies p with a generative and a discriminative component and latent z p(x, y, z) = p(y|x, z) · p(x, z). The p(y|x, z), p(x, z) can be very general; they only share latent z, not parameters! 2We train both components using a multi-conditional objective α · Eq(x,y)Eq(z|x) ` (y, p(y|x, z)) } {{ } discriminative loss (`2, log) +β ·Df [q(x, z)||p(x, z)] } {{ } f-divergence (KL, JS) where q(x, y) is data distribution and α, β > 0 are hyper-parameters.
Distributed Data Mining with Differential Privacy

With recent advances in communication and data storage technology, an explosive amount of information is being collected and stored in the Internet. Even though such vast amount of information presents great opportunities for knowledge discovery, organizations might not want to share their data due to legal or competitive reasons. This posts the challenge of mining knowledge while preserving privacy. Current efficient privacy-preserving data mining algorithms are based on an assumption that it is acceptable to release all the intermediate results during the data mining operations. However, it has been shown that such intermediate results can still leak private information. In this work, we use differential privacy to quantitatively limit such information leak. Differential privacy is a newly emerged privacy definition that is capable of providing strong measurable privacy guarantees. We propose Secure group Differential private Query(SDQ), a new algorithm that combines techniques from differential privacy and secure multiparty computation. Using decision tree induction as a case study, we show that SDQ can achieve stronger privacy than current efficient secure multiparty computation approach, and better accuracy than current differential privacy approach while maintaining efficiency.
Product recommendation with temporal dynamics

In many E-commerce recommender systems, a special class of recommendation involves recommending items to users in a life cycle. For example, customers who have babies will shop on Diapers.com within a relatively long period, and purchase different products for babies within different growth stages. Traditional recommendation algorithms produce recommendation lists similar to items that the target user has accessed before (content filtering), or compute recommendation by analyzing the items purchased by the users who are similar to the target user (collaborative filtering). Such recommendation paradigms cannot effectively resolve the situation with a life cycle, i.e., the need of customers within different stages might vary significantly. In this paper, we model users’ behavior with life cycles by employing handcrafted item taxonomies, of which the background knowledge can be tailored for the computation of personalized recommendation. In particular, our method first formalizes a user’s long-term behavior using the item taxonomy, and then identifies the exact stage of the user. By incorporating collaborative filtering into recommendation, we can easily provide a personalized item list to the user through other similar users within the same stage. An empirical evaluation conducted on a purchasing data collection obtained from Diapers.com demonstrates the efficacy of our proposed method.
Energy efficiency in industry 4.0 using SDN

Industry 4.0 is a new concept that has emerged from the use of new technologies, which brings great improvements to industries. Consequently, several challenges have been launched for this new concept to become consolidated and will become an industry standard. Software Defined Networking (SDN) is a technology that comes with a proposal of flexibility and agility for the communication networks, which fits with the challenges of Industry 4.0, it searches for these requirements throughout the manufacturing process of its products. While industrial evolution is a worldwide trend, Energy Efficiency in industry plays a key role in environmental sustainability and economic performance of the industry. This article presents the APIs using the SDN paradigm to achieve Energy Efficiency applied to Industry 4.0. Simulation results validate and show the benefits of using SDN APIs in Industry 4.0.
Friending your way up the ladder: Connecting massive multiplayer online game behaviors with offline leadership

In what ways do the online behaviors of wizards and ogres map to players’ actual leadership status in the offline world? What can we learn from players’ experience in Massively Multiplayer Online games (MMOGs) to advance our understanding of leadership, especially leadership in online settings (E-leadership)? As part of a larger agenda in the emerging field of empirically testing the ‘‘mapping’’ between the online and offline worlds, this study aims to tackle a central issue in the E-leadership literature: how have technology and technology mediated communications transformed leadership-diagnostic traits and behaviors? To answer this question, we surveyed over 18,000 players of a popular MMOG and also collected behavioral data of a subset of survey respondents over a four-month period. Motivated by leadership theories, we examined the connection between respondents’ offline leadership status and their in-game relationship-oriented and task-related-behaviors. Our results indicate that individuals’ relationship-oriented behaviors in the virtual world are particularly relevant to players’ leadership status in voluntary organizations, while their task-oriented behaviors are marginally linked to offline leadership status in voluntary organizations, but not in companies.
A frequency tunable origami spherical helical antenna

This paper presents a novel origami spherical helical antenna. This antenna operates in normal mode and its operating frequency is tunable from 1.77 GHz to 2.32 GHz for applications of TV or Radio stations. By tuning the scaling factor of each step on the origami pattern, the fabricating process can be greatly simplified, and this antenna can be fabricated at a low cost on light-weight planar substrate.
Tools for educational data mining

In recent years, a wide array of tools have emerged for the purposes of conducting educational data mining (EDM) and/or learning analytics (LA) research. In this article, we hope to highlight some of the most widely used, most accessible, and most powerful tools available for the researcher interested in conducting EDM/LA research. We will highlight the utility that these tools have with respect to common data preprocessing and analysis steps in a typical research project as well as more descriptive information such as price point and userfriendliness. We will also highlight niche tools in the field, such as those used for Bayesian knowledge tracing (BKT), data visualization, text analysis, and social network analysis. Finally, we will discuss the importance of familiarizing oneself with multiple tools—a data analysis toolbox—for the practice of EDM/LA research.
Information Extraction in Illicit Web Domains

Extracting useful entities and attribute values from illicit domains such as human tra cking is a challenging problem with the potential for widespread social impact. Such domains employ atypical language models, have ‘long tails’ and su↵er from the problem of concept drift. In this paper, we propose a lightweight, feature-agnostic Information Extraction (IE) paradigm specifically designed for such domains. Our approach uses raw, unlabeled text from an initial corpus, and a few (12-120) seed annotations per domainspecific attribute, to learn robust IE models for unobserved pages and websites. Empirically, we demonstrate that our approach can outperform feature-centric Conditional Random Field baselines by over 18% F-Measure on five annotated sets of real-world human tra cking datasets in both low-supervision and high-supervision settings. We also show that our approach is demonstrably robust to concept drift, and can be e ciently bootstrapped even in a serial computing environment.
Real-Time Swing-up of Double Inverted Pendulum by Nonlinear Model Predictive Control

In this study, the swing-up of a double inverted pendulum is controlled by nonlinear model predictive control (NMPC). The fast computation algorithm called C/GMRES (continuation/generalized minimal residual) is applied to solve a nonlinear two-point boundary value problem over a receding horizon in real time. The goal is to swing-up and stabilize two pendulums from the downward to upright position. To make the tuning process of the performance index simpler, the terminal cost in the performance index is given by a solution of the algebraic Riccati equation. The simulation results show that C/GMRES can solve the NMPC problem in real time and swingup the double inverted pendulum with a significant reduction in the computational cost compared with Newton’s method.
An Approach to Existence of Fixed Points of Generalized Contractive Multivalued Mappings of Integral Type via Admissible Mapping

and Applied Analysis 3 Definition 12. Let (X, d) be a metric space. We say that G : X → CL(X) is a subintegral type (α∗, ψ)-contractive if there exist two functions ψ ∈ Ψ and φ ∈ Φ s such that for each x ∈ X and y ∈ Gx, there exists z ∈ Gy satisfying
A review of facial gender recognition

Applications such as human–computer interaction, surveillance, biometrics and intelligent marketing would benefit greatly from knowledge of the attributes of the human subjects under scrutiny. The gender of a person is one such significant demographic attribute. This paper provides a review of facial gender recognition in computer vision. It is certainly not a trivial task to identify gender from images of the face. We highlight the challenges involved, which can be divided into human factors and those introduced during the image capture process. A comprehensive survey of facial feature extraction methods for gender recognition studied in the past couple of decades is provided. We appraise the datasets used for evaluation of gender classification performance. Based on the results reported, good performance has been achieved for images captured under controlled environments, but certainly there is still much work that can be done to improve the robustness of gender recognition under real-life environments.
The PROMPT suite: interactive tools for ontology merging and mapping

Researchers in the ontology-design field have developed the content for ontologies in many domain areas. This distributed nature of ontology development has led to a large number of ontologies covering overlapping domains. In order for these ontologies to be reused, they first need to be merged or aligned to one another. We developed a suite of tools for managing multiple ontologies. These suite provides users with a uniform framework for comparing, aligning, and merging ontologies, maintaining versions, translating between different formalisms. Two of the tools in the suite support semi-automatic ontology merging: iPrompt is an interactive ontologymerging tool that guides the user through the merging process, presenting him with suggestions for next steps and identifying inconsistencies and potential problems. AnchorPrompt uses a graph structure of ontologies to find correlation between concepts and to provide additional information for iPrompt. 1 1 Managing Multiple Ontologies Researchers have pursued development of ontologies—explicit formal specifications of domains of discourse—on the premise that ontologies facilitate knowledge sharing and reuse (Musen, 1992; Gruber, 1993). Today, ontology development is moving from academic knowledge-representation projects to the world of e-commerce. Companies use ontologies to share information and to guide customers through their Web sites. Ontologies on the World-Wide Web range from large taxonomies categorizing Web sites (such as Yahoo!) to categorizations of products for sale and their features (such as Amazon.com). In an effort to enable machine-interpretable representation of knowledge on the Web, the WWW Consortium has developed the Resource Description Framework (W3C, 2000), a language for encoding semantic information on Web pages. The WWW consortium is also working on OWL, a more high-level language for semantic annotation on the Web.1 Such encoding makes it possible for electronic agents searching for information to share the common understanding of the semantics of the data represented on the Web. Many disciplines now develop standardized ontologies that domain experts can use to share and annotate information in their fields. Medicine, for example, has produced large, standardized, structured vocabularies such as SNOMED (Spackman, 2000) and the semantic network of the Unified Medical Language System (Lindberg et al., 1993). Broad general-purpose ontologies are emerging as well. For example, the United Nations Development Program and Dun & Bradstreet combined their efforts to develop the UNSPSC ontology which provides terminology for products and services (www.unspsc.org). With this widespread distributed use of ontologies, different parties inevitably develop ontologies http://www.w3.org/2001/sw/WebOnt/
Mass Displacement Networks

The input-output mapping defined by Eq. 1 of the main manuscript is differentiable with respect to both input functions, o(x), c(x), and as such lends itself to end-to-end training with back-propagation. Given a gradient signal δ (·)= ∂L ∂m(·) that dictates how the output layer activations should change to decrease the loss L, we obtain the update equations for c(·) and o(·)=(ox(·),oy(·)) through the following chain rule:
Blockchain Versus Database: A Critical Analysis

In recent times, Bitcoin has gained special attention both from industry and academia. The underlying technology that enables Bitcoin (or more generally crypto-currency) is called blockchain. At the core of the blockchain technology is a data structure that keeps record of the transactions in the network. The special feature that distinguishes it from existing technology is its immutability of the stored records. To achieve immutability, it uses consensus and cryptographic mechanisms. As the data is stored in distributed nodes this technology is also termed as "Distributed Ledger Technology (DLT)". As many researchers and practitioners are joining the hype of blockchain, some of them are raising the question about the fundamental difference between blockchain and traditional database and its real value or potential. In this paper, we present a critical analysis of both technologies based on a survey of the research literature where blockchain solutions are applied to various scenarios. Based on this analysis, we further develop a decision tree diagram that will help both practitioners and researchers to choose the appropriate technology for their use cases. Using our proposed decision tree we evaluate a sample of the existing works to see to what extent the blockchain solutions have been used appropriately in the relevant problem domains.
A Comparative Study on Steganography Digital Images: A Case Study of Scalable Vector Graphics (SVG) and Portable Network Graphics (PNG) Images Formats

Today image steganography plays a key role for exchanging a secret data through the internet. However, the optimal choice of images formats for processing steganography is still an open issue; therefore, this research comes into a table. This research conducts a comparative study between Scalable Vector Graphics (SVG) image format and Portable Network Graphics (PNG) image format. As results show, SVG image format is more efficient than PNG image format in terms of capacity and scalability before and after processing steganography. As well, SVG image format helps to increase simplicity and performance for processing steganography, since it is an XML text file. Our comparative study provides significant results between SVG and PNG images, which have not been seen in the previous related studies. Image steganography; data hiding; raster and vector images; Scalable Vector Graphics (SVG) and Portable Network Graphics (PNG) images format
Adaptive landmark recommendations for travel planning: Personalizing and clustering landmarks using geo-tagged social media

When travelers plan trips, landmark recommendation systems that consider the trip properties will conveniently aid travelers in determining the locations they will visit. Because interesting locations may vary based on the traveler and the situation, it is important to personalize the landmark recommendations by considering the traveler and the trip. In this paper, we propose an approach that adaptively recommends clusters of landmarks using geo-tagged social media.We first examine the impact of a trip’s spatial and temporal properties on the distribution of popular places through large-scale data analyses. In our approach, we compute the significance of landmarks for travelers based on their trip’s spatial and temporal properties. Next, we generate clusters of landmark recommendations, which have similar themes or are contiguous, using travel trajectory histories. Landmark recommendation performances based on our approach are evaluated against several baseline approaches. Our approach results in increased accuracy and satisfaction compared with the baseline approaches. Through a user study, we also verify that our approach is applicable to lesser-known places and reflects local events as well as seasonal changes.
Detection of Cyber-Aggressive Comments on Social Media Networks: A Machine Learning and Text mining approach

The spread of aggressive tweets, status and comments on social network are increasing gradually. People are using social media networks as a virtual platform to troll, objurgate, blaspheme and revile one another. These activities are spreading animosity in race-to-race, religion to religion etc. So, these comments should be identified and blocked on social networks. This work focuses on extracting comments from social networks and analyzes those comments whether they convey any blaspheme or revile in meaning. Comments are classified into three distinct classes; offensive, hate speech and neither. Document similarity analyses are done to identify the correlations among the documents. A well defined text pre-processing analysis is done to create an optimized word vector to train the classification model. Finally, the proposed model categorizes the comments into their respective classes with more than 93% accuracy.
A New Chatbot for Customer Service on Social Media

Users are rapidly turning to social media to request and receive customer service; however, a majority of these requests were not addressed timely or even not addressed at all. To overcome the problem, we create a new conversational system to automatically generate responses for users requests on social media. Our system is integrated with state-of-the-art deep learning techniques and is trained by nearly 1M Twitter conversations between users and agents from over 60 brands. The evaluation reveals that over 40% of the requests are emotional, and the system is about as good as human agents in showing empathy to help users cope with emotional situations. Results also show our system outperforms information retrieval system based on both human judgments and an automatic evaluation metric.
SecDevOps: Is It a Marketing Buzzword? - Mapping Research on Security in DevOps

DevOps is changing the way organizations develop and deploy applications and service customers. Many organizations want to apply DevOps, but they are concerned by the security aspects of the produced software. This has triggered the creation of the terms SecDevOps and DevSecOps. These terms refer to incorporating security practices in a DevOps environment by promoting the collaboration between the development teams, the operations teams, and the security teams. This paper surveys the literature from academia and industry to identify the main aspects of this trend. The main aspects that we found are: definition, security best practices, compliance, process automation, tools for SecDevOps, software configuration, team collaboration, availability of activity data and information secrecy. Although the number of relevant publications is low, we believe that the terms are not buzzwords, they imply important challenges that the security and software communities shall address to help organizations develop secure software while applying DevOps processes.
Quasi-Steady Model of a Pumping Kite Power System

The traction force of a kite can be used to drive a cyclic motion for extracting wind energy from the atmosphere. This paper presents a novel quasi-steady modelling framework for predicting the power generated over a full pumping cycle. The cycle is divided into traction, retraction and transition phases, each described by an individual set of analytic equations. The effect of gravity on the airborne system components is included in the framework. A trade-off is made between modelling accuracy and computation speed such that the model is specifically useful for system optimisation and scaling in economic feasibility studies. Simulation results are compared to experimental measurements of a 20 kW kite power system operated up to a tether length of 720 m. Simulation and experiment agree reasonably well, both for moderate and for strong wind conditions, indicating that the effect of gravity has to be taken into account for a predictive performance simulation.