nils-herrmann/clean_scirepeval huggingface dataset:
Zika: the origin and spread of a mosquito-borne virus

To describe the temporal and geographical distribution of Zika virus infection and associated neurological disorders, from 1947 to 1 February 2016, when Zika became a Public Health Emergency of International Concern (PHEIC).



We did a literature search using the terms "Zika" and "ZIKV" in PubMed, cross-checked the findings for completeness against other published reviews and added formal notifications to WHO submitted under the International Health Regulations.



From the discovery of Zika virus in Uganda in 1947 to the declaration of a PHEIC by the World Health Organization (WHO) on 1 February 2016, a total of 74 countries and territories had reported human Zika virus infections. The timeline in this paper charts the discovery of the virus (1947), its isolation from mosquitos (1948), the first human infection (1952), the initial spread of infection from Asia to a Pacific island (2007), the first known instance of sexual transmission (2008), reports of Guillain-Barré syndrome (2014) and microcephaly (2015) linked to Zika infections and the first appearance of Zika in the Americas (from 2015).



Zika virus infection in humans appears to have changed in character as its geographical range has expanded from equatorial Africa and Asia. The change is from an endemic, mosquito-borne infection causing mild illness to one that can cause large outbreaks linked with neurological sequelae and congenital abnormalities.
Software Design and Architecture The once and future focus of software engineering

The design of software has been a focus of software engineering research since the field's beginning. This paper explores key aspects of this research focus and shows why design will remain a principal focus. The intrinsic elements of software design, both process and product, are discussed: concept formation, use of experience, and means for representation, reasoning, and directing the design activity. Design is presented as being an activity engaged by a wide range of stakeholders, acting throughout most of a system's lifecycle, making a set of key choices which constitute the application's architecture. Directions for design research are outlined, including: (a) drawing lessons, inspiration, and techniques from design fields outside of computer science, (b) emphasizing the design of application "character" (functionality and style) as well as the application's structure, and (c) expanding the notion of software to encompass the design of additional kinds of intangible complex artifacts.
Fast and accurate modeling of molecular atomization energies with machine learning.

We introduce a machine learning model to predict atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only. The problem of solving the molecular Schrödinger equation is mapped onto a nonlinear statistical regression problem of reduced complexity. Regression models are trained on and compared to atomization energies computed with hybrid density-functional theory. Cross validation over more than seven thousand organic molecules yields a mean absolute error of ∼10  kcal/mol. Applicability is demonstrated for the prediction of molecular atomization potential energy curves.
A feature clustering approach based on Histogram of Oriented Optical Flow and superpixels

Visual feature clustering is one of the cost-effective approaches to segment objects in videos. However, the assumptions made for developing the existing algorithms prevent them from being used in situations like segmenting an unknown number of static and moving objects under heavy camera movements. This paper addresses the problem by introducing a clustering approach based on superpixels and short-term Histogram of Oriented Optical Flow (HOOF). Salient Dither Pattern Feature (SDPF) is used as the visual feature to track the flow and Simple Linear Iterative Clustering (SLIC) is used for obtaining the superpixels. This new clustering approach is based on merging superpixels by comparing short term local HOOF and a color cue to form high-level semantic segments. The new approach was compared with one of the latest feature clustering approaches based on K-Means in eight-dimensional space and the results revealed that the new approach is better by means of consistency, completeness, and spatial accuracy. Further, the new approach completely solved the problem of not knowing the number of objects in a scene.
Adapting GOMS to model human-robot interaction

A formal interaction modeling technique known as Goals, Operators, Methods, and Selection rules (GOMS) is well-established in human-computer interaction as a cost-effective way of evaluating designs without the participation of end users. This paper explores the use of GOMS for evaluating human-robot interaction. We provide a case study in the urban search-and-rescue domain and raise issues for developing GOMS models that have not been previously addressed. Further, we provide rationale for selecting different types of GOMS modeling techniques to help the analyst model human-robot interfaces.
Energy-Aware Autonomic Resource Allocation in Multitier Virtualized Environments

With the increase of energy consumption associated with IT infrastructures, energy management is becoming a priority in the design and operation of complex service-based systems. At the same time, service providers need to comply with Service Level Agreement (SLA) contracts which determine the revenues and penalties on the basis of the achieved performance level. This paper focuses on the resource allocation problem in multitier virtualized systems with the goal of maximizing the SLAs revenue while minimizing energy costs. The main novelty of our approach is to address-in a unifying framework-service centers resource management by exploiting as actuation mechanisms allocation of virtual machines (VMs) to servers, load balancing, capacity allocation, server power state tuning, and dynamic voltage/frequency scaling. Resource management is modeled as an NP-hard mixed integer nonlinear programming problem, and solved by a local search procedure. To validate its effectiveness, the proposed model is compared to top-performing state-of-the-art techniques. The evaluation is based on simulation and on real experiments performed in a prototype environment. Synthetic as well as realistic workloads and a number of different scenarios of interest are considered. Results show that we are able to yield significant revenue gains for the provider when compared to alternative methods (up to 45 percent). Moreover, solutions are robust to service time and workload variations.
SE-AKA: A secure and efficient group authentication and key agreement protocol for LTE networks

To support Evolved Packet System (EPS) in the Long Term Evolution (LTE) networks, the 3rd Generation Partnership Project (3GPP) has proposed an authentication and key agreement (AKA) protocol, named EPS-AKA, which has become an emerging standard for fourth-generation (4G) wireless communications. However, due to the requirement of backward compatibility, EPS-AKA inevitably inherits some defects of its predecessor UMTS-AKA protocol that cannot resist several frequent attacks, i.e., redirection attack, man-in-the-middle attack, and DoS attack. Meanwhile, there are additional security issues associated with the EPS-AKA protocol, i.e., the lack of privacy-preservation and key forward/backward secrecy (KFS/KBS). In addition, there are new challenges with the emergence of groupbased communication scenarios in authentication. In this paper, we propose a secure and efficient AKA protocol, called SE-AKA, which can fit in with all of the group authentication scenarios in the LTE networks. Specifically, SE-AKA uses Elliptic Curve Diffie-Hellman (ECDH) to realize KFS/KBS, and it also adopts an asymmetric key cryptosystem to protect users’ privacy. For group authentication, it simplifies the whole authentication procedure by computing a group temporary key (GTK). Compared with other authentication protocols, SE-AKA cannot only provide strong security including privacy-preservation and KFS/KBS, but also provide a group authentication mechanism which can effectively authenticate group devices. Extensive security analysis and formal verification by using proverif have shown that the proposed SE-AKA is secure against various malicious attacks. In addition, elaborate performance evaluations in terms of communication, computational and storage overhead also demonstrates that SE-AKA is more efficient than those existing protocols.
Node Classification in Signed Social Networks

Node classification in social networks has been proven to be useful in many real-world applications. The vast majority of existing algorithms focus on unsigned social networks (or social networks with only positive links), while little work exists for signed social networks. It is evident from recent developments in signed social network analysis that negative links have added value over positive links. Therefore, the incorporation of negative links has the potential to benefit various analytical tasks. In this paper, we study the novel problem of node classification in signed social networks. We provide a principled way to mathematically model positive and negative links simultaneously and propose a novel framework NCSSN for node classification in signed social networks. Experimental results on real-world signed social network datasets demonstrate the effectiveness of the proposed framework NCSSN. Further experiments are conducted to gain a deeper understanding of the importance of negative links for NCSSN.
An efficient FPGA-Based architecture for convolutional neural networks

The goal of this paper is to implement an efficient FPGA-based hardware architectures for the design of fast artificial vision systems. The proposed architecture is capable of performing classification operations of a Convolutional Neural Network (CNN) in realtime. To show the effectiveness of the architecture, some design examples such as hand posture recognition, character recognition, and face recognition are provided. Experimental results show that the proposed architecture is well suited for embedded artificial computer vision systems requiring high portability, high computational speed, and accurate classification.
Neural mechanisms of selective visual attention.

The two basic phenomena that define the problem of visual attention can be illustrated in a simple example. Consider the arrays shown in each panel of Figure 1. In a typical experiment, before the arrays were presented, subjects would be asked to report letters appearing in one color (targets, here black letters), and to disregard letters in the other color (nontargets, here white letters). The array would then be briefly flashed, and the subjects, without any opportunity for eye movements, would give their report. The display mimics our. usual cluttered visual environment: It contains one or more objects that are relevant to current behavior, along with others that are irrelevant. The first basic phenomenon is limited capacity for processing information. At any given time, only a small amount of the information available on the retina can be processed and used in the control of behavior. Subjectively, giving attention to any one target leaves less available for others.  than with none (Figure Ib). The second basic phenomenon is selectivity-the ability to filter out un­ wanted information. Subjectively, one is aware of attended stimuli and largely unaware of unattended ones. Correspondingly, accuracy in identifying an attended stimulus may be independent of the number of nontargets in a display (Figure la vs Ie) (see Bundesen 1990, Duncan 1980).
Timeline: A Dynamic Hierarchical Dirichlet Process Model for Recovering Birth/Death and Evolution of Topics in Text Stream

Topic models have proven to be a useful tool for discovering latent structures in document collections. However, most document collections often come as temporal streams and thus several aspects of the latent structure such as the number of topics, the topics’ distribution and popularity are time-evolving. Several models exist that model the evolution of some but not all of the above aspects. In this paper we introduce infinite dynamic topic models, iDTM, that can accommodate the evolution of all the aforementioned aspects. Our model assumes that documents are organized into epochs, where the documents within each epoch are exchangeable but the order between the documents is maintained across epochs. iDTM allows for unbounded number of topics: topics can die or be born at any epoch, and the representation of each topic can evolve according to a Markovian dynamics. We use iDTM to analyze the birth and evolution of topics in the NIPS community and evaluated the efficacy of our model on both simulated and real datasets with favorable outcome.
Deduplication on Encrypted Big Data in Cloud

Cloud computing offers a new way of service provision by re-arranging various resources over the Internet. The most important and popular cloud service is data storage. In order to preserve the privacy of data holders, data are often stored in cloud in an encrypted form. However, encrypted data introduce new challenges for cloud data deduplication, which becomes crucial for big data storage and processing in cloud. Traditional deduplication schemes cannot work on encrypted data. Existing solutions of encrypted data deduplication suffer from security weakness. They cannot flexibly support data access control and revocation. Therefore, few of them can be readily deployed in practice. In this paper, we propose a scheme to deduplicate encrypted data stored in cloud based on ownership challenge and proxy re-encryption. It integrates cloud data deduplication with access control. We evaluate its performance based on extensive analysis and computer simulations. The results show the superior efficiency and effectiveness of the scheme for potential practical deployment, especially for big data deduplication in cloud storage.
Explaining consumer satisfaction of services: The role of innovativeness and emotion in an electronic mediated environment

a r t i c l e i n f o The services section has started to dominate economic activity in many industrialized economies since the last decade. The growth of services in Electronic Mediated Environment (EME) has changed the manner in which firms and consumers interact. Drawing on such theoretical foundations as trust, risk perceptions, trying, emotion, and satisfaction, this study proposes an integrative research model to investigate the relationships among these constructs in the EME context. We collected data from 415 consumers and found that consumer innovativeness and emotion are significant antecedents of trust, risk perception of service, perceived benefits, and service quality. These important factors will influence consumer satisfaction in EME. This empirical investigation breaks a new ground for future studies to understand and gauge the influence of innovativeness and emotion in emerging IT artifacts such as EME services. The service sector has become quantitatively the most essential sector in the world economy over the last decade. It now leads economic activities in a wide variety of industrialized economies. According to CIA World Factbook , the service industry accounted for 76.8% and 73.1% of the U.S. and European Union gross domestic product in 2011, respectively. Concurrently, there are more service companies on the list of Future 500 companies than before. Given the precipitously growing rate of the service section, technologically empowered advancements of information technology (IT) and the ever developing Internet are constantly galvanizing today's organizations to more effectively and efficiently coordinate with their constituents via the value-added electronic services (e-services) conduit. Furthermore, the mushrooming growth of e-commerce reflects the increasingly paramount role of IT in e-services while IT spurs the influx of e-service economy across the Internet. It is apparent that Information Systems (IS) plays a crucial role in EME because contemporary organizations have to leverage IT to improve business performance and assist the communication and service delivery amid various value-chain constituents. Consumers play a focal role in the paradigmatic change from the exchange of goods towards a service-centered model of exchange as found in both IS and marketing literature [73]. In this study, the conceptual definition of services in EME is based on Dai and Salam [19] and thereafter refers to the " services being facilitated by IT where the consumer interacts with a proper user interface including web site, mobile phone, social media, virtual world environment, tablets, etc., so as to gain a consumption …
Color , Shape and Texture based Fruit Recognition System

The paper presents an automated system for classification of fruits. A dataset containing five different fruits was constructed using an ordinary camera. All the fruits were analyzed on the basis of their color (RGB space), shape and texture and then classified using different classifiers to find the classifier that gives the best accuracy. Gray Level Cooccurance Matrix(GLCM) is used to calculate texture features .Best accuracy was achieved by support vector machine (svm). All the processing was carried out in Matlab. computer vision, pattern recognition, support vector machine, texture features.
Suicide and Self-Harm by Drowning: A Review of the Literature.

The objectives of this study were to carry out a comprehensive review of the worldwide literature on suicidal behaviour by drowning. Systematic electronic searches of databases using various search terms were carried out. Recent trends in suicide and undetermined deaths due to drowning in England and Wales are described. The characteristics of patients presenting to the general hospital in Oxford, UK following attempted drowning are compared with self-poisoning patients. A total of 20 studies containing empirical data about suicide by drowning were identified, mainly concerning Western countries. Drowning suicides have declined in most countries in recent years. The proportion of undetermined deaths remains high. Drowning suicides and self-harm patients tend to be older, with only a small excess of males compared to those using other methods. This is an under-researched area that deserves good quality studies focusing upon prevention.
Secure Wallet-Assisted Offline Bitcoin Payments with Double-Spender Revocation

Bitcoin seems to be the most successful cryptocurrency so far given the growing real life deployment and popularity. While Bitcoin requires clients to be online to perform transactions and a certain amount of time to verify them, there are many real life scenarios that demand for offline and immediate payments (e.g., mobile ticketing, vending machines, etc). However, offline payments in Bitcoin raise non-trivial security challenges, as the payee has no means to verify the received coins without having access to the Bitcoin network. Moreover, even online immediate payments are shown to be vulnerable to double-spending attacks. In this paper, we propose the first solution for Bitcoin payments, which enables secure payments with Bitcoin in offline settings and in scenarios where payments need to be immediately accepted. Our approach relies on an offline wallet and deploys several novel security mechanisms to prevent double-spending and to verify the coin validity in offline setting. These mechanisms achieve probabilistic security to guarantee that the attack probability is lower than the desired threshold. We provide a security and risk analysis as well as model security parameters for various adversaries. We further eliminate remaining risks by detection of misbehaving wallets and their revocation.
 We implemented our solution for mobile Android clients and instantiated an offline wallet using a microSD security card. Our implementation demonstrates that smooth integration over a very prevalent platform (Android) is possible, and that offline and online payments can practically co-exist. We also discuss alternative deployment approach for the offline wallet which does not leverage secure hardware, but instead relies on a deposit system managed by the Bitcoin network.
Modelling Valence and Arousal in Facebook posts

Access to expressions of subjective personal posts increased with the popularity of Social Media. However, most of the work in sentiment analysis focuses on predicting only valence from text and usually targeted at a product, rather than affective states. In this paper, we introduce a new data set of 2895 Social Media posts rated by two psychologicallytrained annotators on two separate ordinal nine-point scales. These scales represent valence (or sentiment) and arousal (or intensity), which defines each post’s position on the circumplex model of affect, a well-established system for describing emotional states (Russell, 1980; Posner et al., 2005). The data set is used to train prediction models for each of the two dimensions from text which achieve high predictive accuracy – correlated at r = .65 with valence and r = .85 with arousal annotations. Our data set offers a building block to a deeper study of personal affect as expressed in social media. This can be used in applications such as mental illness detection or in automated large-scale psychological studies.
Beyond caption to narrative: Video captioning with multiple sentences

Recent advances in image captioning task have led to increasing interests in video captioning task. However, most works on video captioning are focused on generating single input of aggregated features, which hardly deviates from image captioning process and does not fully take advantage of dynamic contents present in videos. We attempt to generate video captions that convey richer contents by temporally segmenting the video with action localization, generating multiple captions from multiple frames, and connecting them with natural language processing techniques, in order to generate a story-like caption. We show that our proposed method can generate captions that are richer in contents and can compete with state-of-the-art method wRecent advances in image captioning task have led to increasing interests in video captioning task. However, most works on video captioning are focused on generating single input of aggregated features, which hardly deviates from image captioning process and does not fully take advantage of dynamic contents present in videos. We attempt to generate video captions that convey richer contents by temporally segmenting the video with action localization, generating multiple captions from multiple frames, and connecting them with natural language processing techniques, in order to generate a story-like caption. We show that our proposed method can generate captions that are richer in contents and can compete with state-of-the-art method without explicitly using video-level features as input.