nils-herrmann/clean_scirepeval huggingface dataset:
Microsoft COCO Captions: Data Collection and Evaluation Server

In this paper we describe the Microsoft COCO Caption dataset and evaluation server. When completed, the dataset will contain over one and a half million captions describing over 330,000 images. For the training and validation images, five independent human generated captions will be provided. To ensure consistency in evaluation of automatic caption generation algorithms, an evaluation server is used. The evaluation server receives candidate captions and scores them using several popular metrics, including BLEU, METEOR, ROUGE and CIDEr. Instructions for using the evaluation server are provided.
Adaptive Mobile Cloud Computing to Enable Rich Mobile Multimedia Applications

With worldwide shipments of smartphones (487.7 million) exceeding PCs (414.6 million including tablets) in 2011, and in the US alone, more users predicted to access the Internet from mobile devices than from PCs by 2015, clearly there is a desire to be able to use mobile devices and networks like we use PCs and wireline networks today. However, in spite of advances in the capabilities of mobile devices, a gap will continue to exist, and may even widen, with the requirements of rich multimedia applications. Mobile cloud computing can help bridge this gap, providing mobile applications the capabilities of cloud servers and storage together with the benefits of mobile devices and mobile connectivity, possibly enabling a new generation of truly ubiquitous multimedia applications on mobile devices: Cloud Mobile Media (CMM) applications.
Property Rights Theory , Transaction Costs Theory , and Agency Theory : An Organizational Economics Approach to Strategic Management

Property rights theory has common antecedents with contractual theories of the firm such as transaction costs and agency theories, and is yet distinct from these theories. We illustrate fundamental theoretical principles derived from these three theories by analyzing the business case of oil field unitization. Theoretical principles and application of theory to oil field unitization are each summarized. From this, it is possible to see how property rights theory is well suited to explain business situations where inefficient economic outcomes persist. Additionally, property rights theory forges new theoretical connections with other branches of organizational economics, in particular, resource-based theory.
A Unified Control for the DC–AC Interlinking Converters in Hybrid AC/DC Microgrids

A novel unified control of the dc–ac interlinking converters (ICs) for autonomous operation of hybrid ac/dc microgrids (MGs) has been proposed in this paper. When the slack terminals in the ac and dc MGs are available, the ICs will operate in autonomous control of interlinking power between the ac and dc subgrids, with the total load demand proportionally shared among the existing ac and dc slack terminals. With a flexible control variable added in power control loop, design of the interlinking power control, and droop features of ac and dc MGs can be decoupled. Moreover, this control variable can be tuned flexibly according to different power control objectives, such as proportional power sharing in terms of capacity (which is considered in this paper), interlinking power dispatch, and other optimal power dispatch algorithms, ensuring a well-designed flexibility and compatibility. Furthermore, if the dc MG or the ac MG loses dc voltage control or ac voltage and frequency control capability due to failures of operation of its slack terminals, the ICs can automatically and seamlessly transfer to dc MG support or ac MG support control modes without operation mode detection, communication, control scheme switching, and control saturation. In order to enhance the stability of the proposed unified control in different modes with different control plants, a phase compensation transfer function has been added in the power control loop. After thorough theoretical analysis and discussions, detailed simulation verifications based on PSCAD/EMTDC and experimental results based on a hardware experimental MG platform have been presented.
Identifying users across social networks based on dynamic core interests

With the development of social networks, most of users hold serval accounts in different social network platforms. It is a very important task to match users' varying identities in the internet. Plenty of existing approaches attempt to link users via comparing social structures, mapping users' profiles and analyzing users' authority. Those existing approaches fail to consider the dynamic changes of users. In the paper, we introduce human behavioral limitations in social networks. And then based on the limitations, we propose a dynamic core interests mapping (DCIM) algorithm, which jointly consider the users' social network structures and users' article content to identify users over platforms. The algorithm firstly models user's core interests and then calculates the similarity of two target users using DCIM. Our experiments use real world datasets from Twitter and BlogCatalog. The results of experiments show that our method is effective on mapping users across social networks. And the algorithm is significantly more effective than baseline methods such as FNN and MAG. & 2016 Elsevier B.V. All rights reserved.
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms

Comparisons of CNN, LSTM and SWEM architectures. Columns correspond to the number of compositional parameters, computational complexity and sequential operations, respectively. v Consider a text sequence represented as X, composed of a sequence of words. Let {v#, v$, ...., v%} denote the respective word embeddings for each token, where L is the sentence/document length; v The compositional function, X → z, aims to combine word embeddings into a fixed-length sentence/document representation z. Typically, LSTM or CNN are employed for this purpose;
Multimodal Transfer: A Hierarchical Deep Convolutional Neural Network for Fast Artistic Style Transfer

Transferring artistic styles onto everyday photographs has become an extremely popular task in both academia and industry. Recently, offline training has replaced online iterative optimization, enabling nearly real-time stylization. When those stylization networks are applied directly to high-resolution images, however, the style of localized regions often appears less similar to the desired artistic style. This is because the transfer process fails to capture small, intricate textures and maintain correct texture scales of the artworks. Here we propose a multimodal convolutional neural network that takes into consideration faithful representations of both color and luminance channels, and performs stylization hierarchically with multiple losses of increasing scales. Compared to state-of-the-art networks, our network can also perform style transfer in nearly real-time by performing much more sophisticated training offline. By properly handling style and texture cues at multiple scales using several modalities, we can transfer not just large-scale, obvious style cues but also subtle, exquisite ones. That is, our scheme can generate results that are visually pleasing and more similar to multiple desired artistic styles with color and texture cues at multiple scales.
We are Humor Beings: Understanding and Predicting Visual Humor

Humor is an integral part of human lives. Despite being tremendously impactful, it is perhaps surprising that we do not have a detailed understanding of humor yet. As interactions between humans and AI systems increase, it is imperative that these systems are taught to understand subtleties of human expressions such as humor. In this work, we are interested in the question - what content in a scene causes it to be funny? As a first step towards understanding visual humor, we analyze the humor manifested in abstract scenes and design computational models for them. We collect two datasets of abstract scenes that facilitate the study of humor at both the scene-level and the object-level. We analyze the funny scenes and explore the different types of humor depicted in them via human studies. We model two tasks that we believe demonstrate an understanding of some aspects of visual humor. The tasks involve predicting the funniness of a scene and altering the funniness of a scene. We show that our models perform well quantitatively, and qualitatively through human studies. Our datasets are publicly available.
Management of Periodontally Involved Anterior Teeth by Glass Fiber-Reinforced Composite Splinting : A Clinical Report with 5-Year Recall

Periodontal disease results in attachment loss and damage to the supporting alveolar bone leading to tooth mobility. In majority of cases, the mandibular incisors are the teeth showing the first signs of mobility. The clinical management of periodontally involved teeth remains a challenge to the clinician. Splinting may be indicated for individual mobile tooth as well as for the entire dentition. The main objectives of splinting include decreasing patient discomfort, increasing occlusal and masticatory function, enhancing esthetics and improving the periodontal prognosis of mobile teeth. Fiber-reinforced composites provide one of the better alternatives for splinting of teeth. This clinical report describes a technique of splinting of periodontally involved mandibular anterior teeth using glass fiber-reinforced composite resin with a follow-up period of five years.
Artificial Intelligence: Some Legal Approaches and Implications

Various groups of ascertainable individuals have been granted the status of “persons” under American law, while that status has been denied to other groups This article examines various analogies that might be drawn by courts in deciding whether to extend “person” status to intelligent machines, and the limitations that might be placed upon such recognition As an alternative analysis: this article questions the legal status of various human/machine interfaces, and notes the difficulty in establishing an absolute point beyond which legal recognition will not extend COMPUTERS INCREASINGLY RESEMBLE their human creators More precisely, it is becoming increasingly difficult to distinguish some computer information-processing from that of humans, judging from the final product. Computers have proven capable of far more physical and mental “human” functions than most people believed was possible. The increasing similarity between humans and machines might eventually require legal recognition of computers as “persons.” In the United States, there are two triers t’o such Views expressed here are those of the author @ Llarshal S. Willick 1982 41 rights reserved  This article is written by an attorney using a common reference style for legal citations The system of citation is more complex than systems ordinarily used in scientific publications since it must provide numerous variations for different sources of evidence and jurisdictions We have decided not to change t.his article’s format for citations. legal recognition. The first tier determines which ascertainable individuals are considered persons (e g., blacks, yes; fetuses, no.) The second tier determines which rights and obligations are vested in the recognized persons, based on their observed or presumed capacities (e.g., the insane are restricted; eighteen-year-olds can vote.) The legal system is more evolutionary than revolutionary, however. Changes in which individuals should be recognized as persons under the law tend to be in response to changing cult,ural and economic realities, rather than the result of advance planning. Similarly, shifts in the allocation of legal rights and obligations are usually the result of societal pressures that do not result from a dispassionate masterplanning of society. Courts attempt to analogize new problems to those previously settled, where possible: the process is necessarily haphazard. As “intelligent” machines appear, t,hey will pervade a society in which computers play an increasingly significant part, but in which they will have no recognized legal personality. The question of what rights they should have will most probably not have been addressed. It is therefore most likely that computers will enter the legal arena through the courts The myriad acts of countless individuals will eventually give rise to a situat,ion in which some judicial decision regarding computer personality is needed in order to determine the rights of the parties to a THE AI MAGAZINE Summer 1983 5 AI Magazine Volume 4 Number 2 (1983) (
Nastalique segmentation-based approach for Urdu OCR

Much work on Arabic language optical character recognition (OCR) has been on Naskh writing style. Nastalique style, used for most of languages using Arabic script across Southern Asia, is much more challenging to process due to its compactness, cursiveness, higher context sensitivity and diagonality. This makes the Nastalique writing more complex with multiple letters horizontally overlapping each other. Due to these reasons, existing methods used for Naskh would not work for Nastalique and therefore most work on Nastalique has used non-segmentation methods. The current paper presents new approach for segmentation-based analysis for Nastalique style. The paper explains the complexity of Nastalique, why Naskh based techniques cannot work for Nastalique, and proposes a segmentation-based method for developing Nastalique OCR, deriving principles and techniques for the pre-processing and recognition. The OCR is developed for Urdu language. The system is optimized using 79,093 instances of 5249 main bodies derived from a corpus of 18 million words, giving recognition accuracy of 97.11 %. The system is then tested on document images of books with 87.44 % main body recognition accuracy. The work is extensible to other languages using Nastalique.
Public Review for Knowledge-Defined Networking

The research community has considered in the past the application of Artificial Intelligence (AI) techniques to control and operate networks. A notable example is the Knowledge Plane proposed by D.Clark et al. However, such techniques have not been extensively prototyped or deployed in the field yet. In this paper, we explore the reasons for the lack of adoption and posit that the rise of two recent paradigms: Software-Defined Networking (SDN) and Network Analytics (NA), will facilitate the adoption of AI techniques in the context of network operation and control. We describe a new paradigm that accommodates and exploits SDN, NA and AI, and provide use-cases that illustrate its applicability and benefits. We also present simple experimental results that support, for some relevant use-cases, its feasibility. We refer to this new paradigm as Knowledge-Defined Networking (KDN).
THE EFFECTS OF DIGITAL MARKETING COMMuNICATION ON CuSTOMER LOyALTy : AN INTEGRATIvE MODEL AND

The cost efficiency and diversity of digital channels facilitate marketers’ frequent and interactive communication with their customers. Digital channels like the Internet, email, mobile phones and digital television offer new prospects to cultivate customer relationships. However, there are a few models explaining how digital marketing communication (DMC) works from a relationship marketing perspective, especially for cultivating customer loyalty. In this paper, we draw together previous research into an integrative conceptual model that explains how the key elements of DMC frequency and content of brand communication, personalization, and interactivity can lead to improved customer value, commitment, and loyalty.
The Role of Morphology of the Thumb in Anthropomorphic Grasping: A Review

The unique musculoskeletal structure of the human hand brings in wider dexterous capabilities to grasp and manipulate a repertoire of objects than the non-human primates. It has been widely accepted that the orientation and the position of the thumb plays an important role in this characteristic behavior. There have been numerous attempts to develop anthropomorphic robotic hands with varying levels of success. Nevertheless, manipulation ability in those hands is to be ameliorated even though they can grasp objects successfully. An appropriate model of the thumb is important to manipulate the objects against the fingers and to maintain the stability. Modeling these complex interactions about the mechanical axes of the joints and how to incorporate these joints in robotic thumbs is a challenging task. This article presents a review of the biomechanics of the human thumb and the robotic thumb designs to identify opportunities for future anthropomorphic robotic hands.
Word of Mouth: Rumor Dissemination in Social Networks

In this paper we examine the diffusion of competing rumors in social networks. Two players select a disjoint subset of nodes as initiators of the rumor propagation, seeking to maximize the number of persuaded nodes. We use concepts of game theory and location theory and model the selection of starting nodes for the rumors as a strategic game. We show that computing the optimal strategy for both the first and the second player is NP-complete, even in a most restricted model. Moreover we prove that determining an approximate solution for the first player is NP-complete as well. We analyze several heuristics and show that—counter-intuitively—being the first to decide is not always an advantage, namely there exist networks where the second player can convince more nodes than the first, regardless of the first player’s decision.
Zero-Shot Learning via Class-Conditioned Deep Generative Models

We present a deep generative model for Zero-Shot Learning (ZSL). Unlike most existing methods for this problem, that represent each class as a point (via a semantic embedding), we represent each seen/unseen class using a classspecific latent-space distribution, conditioned on class attributes. We use these latent-space distributions as a prior for a supervised variational autoencoder (VAE), which also facilitates learning highly discriminative feature representations for the inputs. The entire framework is learned end-to-end using only the seen-class training data. At test time, the label for an unseen-class test input is the class that maximizes the VAE lower bound. We further extend the model to a (i) semi-supervised/transductive setting by leveraging unlabeled unseen-class data via an unsupervised learning module, and (ii) few-shot learning where we also have a small number of labeled inputs from the unseen classes. We compare our model with several state-of-the-art methods through a comprehensive set of experiments on a variety of benchmark data sets.
A time-dependent proportional hazards survival model for credit risk analysis

In the consumer credit industry, assessment of default risk is critically important for the financial health of both the lender and the borrower. Methods for predicting risk for an applicant using credit bureau and application data, typically based on logistic regression or survival analysis, are universally employed by credit card companies. Because of the manner in which the predictive models are fit using large historical sets of existing customer data that extend over many years, default trends, anomalies, and other temporal phenomena that result from dynamic economic conditions are not brought to light. We introduce a modification of the proportional hazards survival model that includes a time-dependency mechanism for capturing temporal phenomena, and we develop a maximum likelihood algorithm for fitting the model. Using a very large, real data set, we demonstrate that incorporating the time dependency can provide more accurate risk scoring, as well as important insight into dynamic market effects that can inform and enhance related decision making.  (2012) 63, 306–321. doi:10.1057/jors.2011.34 Published online 11 May 2011
Implementation of Smart Contracts Using Hybrid Architectures with On and Off–Blockchain Components

Decentralised (on-blockchain) and centralised (off–blockchain) platforms are available for the implementation of smart contracts. However, none of the two alternatives can individually provide the services and quality of services (QoS) imposed on smart contracts involved in a large class of applications. The reason is that blockchain platforms suffer from scalability, performance, transaction costs and other limitations. Likewise, off–blockchain platforms are afflicted by drawbacks emerging from their dependence on single trusted third parties. We argue that in several applications, hybrid platforms composed from the integration of on and off–blockchain platforms are more adequate. Developers that informatively choose between the three alternatives are likely to implement smart contracts that deliver the expected QoS. Hybrid architectures are largely unexplored. To help cover the gap and as a proof of concept, in this paper we discuss the implementation of smart contracts on hybrid architectures. We show how a smart contract can be split and executed partially on an off–blockchain contract compliance checker and partially on the rinkeby ethereum network. To test the solution, we expose it to sequences of contractual operations generated mechanically by a contract validator tool.
Global Multi-armed Bandits with Hölder Continuity

Standard Multi-Armed Bandit (MAB) problems assume that the arms are independent. However, in many application scenarios, the information obtained by playing an arm provides information about the remainder of the arms. Hence, in such applications, this informativeness can and should be exploited to enable faster convergence to the optimal solution. In this paper, formalize a new class of multi-armed bandit methods, Global Multi-armed Bandit (GMAB), in which arms are globally informative through a global parameter, i.e., choosing an arm reveals information about all the arms. We propose a greedy policy for the GMAB which always selects the arm with the highest estimated expected reward, and prove that it achieves bounded parameter-dependent regret. Hence, this policy selects suboptimal arms only finitely many times, and after a finite number of initial time steps, the optimal arm is selected in all of the remaining time steps with probability one. In addition, we also study how the informativeness of the arms about each other’s rewards affects the speed of learning. Specifically, we prove that the parameter-free (worst-case) regret is sublinear in time, and decreases with the informativeness of the arms. We also prove a sublinear in time Bayesian risk bound for the GMAB which reduces to the well-known Bayesian risk bound for linearly parameterized bandits when the arms are fully informative. GMABs have applications ranging from drug dosage control to dynamic pricing. Appearing in Proceedings of the 18 International Conference on Artificial Intelligence and Statistics (AISTATS) 2015, San Diego, CA, USA.  W&CP volume 38.
Library for Flight Dynamics Modelling of a mini-UAV

This paper presents using of JSBSim library for flight dynamics modelling of a mini-UAV (Unmanned Aerial Vehicle). The first part of the paper is about general information of UAVs and about the fundamentals of airplane flight mechanics, forces, moments, and the main components of typical aircraft. The main section briefly describes a flight dynamics model and summarizes the information about JSBSim library. Then, a way of using the library for the modelling of a mini-UAV is shown. A basic script for lifting and stabilization of the UAV has been developed and described. Finally, the results of JSBSim test are discussed.