nils-herrmann/clean_scirepeval huggingface dataset:
CPB: a classification-based approach for burst time prediction in cascades

Studying the bursty nature of cascades in social media is practically important in many real applications such as product sales prediction, disaster relief, and stock market prediction. Although both the cascade size prediction and the burst patterns of the cascades have been extensively studied, how to predict when a burst will come remains an open problem. It is challenging for traditional time-series-based models such as regression models to address this task directly. Firstly, times-series-based prediction models focus on predicting the future values based on previously observed ones. It is hard to apply them to predict the time of a bursts with the “quick rise-and-fall” pattern. Secondly, besides the cascade popularity, a lot of other side information like user profile and social relation are available in social media. Although the potential utility of such information can be high, it is also hard for time-series-based models to capture and integrate these rich information with diverse formats seamlessly. This paper proposes a classification-based approach for burst time prediction by exploiting rich knowledge in information diffusion. Particularly, we first propose a time-window-based transformation to predict in which time window the burst will appear. By dividing the time spans of all the cascades into the same number of time windows K, the cascades with diverse time spans can thus be handled uniformly. To exploit the rich and heterogenous information in social media, we next propose a scale-independent feature extraction framework to model the heterogenous knowledge in a scale-independent manner. Systematical evaluations are conducted on the Sina Weibo reposting dataset and MemeTracker dataset. Besides the superior performance of the proposed approach, we also observe that: (1) surprisingly, social/structure knowledge is more indicative of the bursts than the cascade popularity information, especially for the bursts occurring in a farther future. (2) Larger cascades are harder to predict as the spreading process of the cascades with higher popularity is usually more diverse and fluctuant. (3) The proposed approach is robust in the sense that the result is not much sensitive to the popularity of the training cascades.
The Distributed Dissimilar Redundancy Architecture of Fly-by-Wire Flight Control System

This article presents a distributed dissimilar redundancy architecture of fly-by-wire Flight Control System. It is based on many dissimilar flight control computer, redundancy structure which includes the cross arranged hydraulic and power supply, three types of data buses, lots of data correction monitors, a distributed control loop to avoid the correlated faults and achieve the rigid safety airworthiness requirements. A Fault Tree Analysis (FTA) is implemented to verify whether the system safety could meet system design targets.
A Survey on the Security of Hypervisors in Cloud Computing

This survey paper focuses on the security of hyper visors in the cloud. Topics covered in this paper include attacks that allow a malicious virtual machine (VM) to compromise the hyper visor, as well as techniques used by malicious VMs to steal more than their allocated share of physical resources, and ways to bypass the isolation between the VMs by using side-channels to steal data. Also discussed are the security requirements and architectures for hyper visors to successfully defend against such attacks.
Using argumentation logic for firewall configuration management

Firewalls remain the main perimeter security protection for corporate networks. However, network size and complexity make firewall configuration and maintenance notoriously difficult. Tools are needed to analyse firewall configurations for errors, to verify that they correctly implement security requirements and to generate configurations from higher-level requirements. In this paper we extend our previous work on the use of formal argumentation and preference reasoning for firewall policy analysis and develop means to automatically generate firewall policies from higher-level requirements. This permits both analysis and generation to be done within the same framework, thus accommodating a wide variety of scenarios for authoring and maintaining firewall configurations. We validate our approach by applying it to both examples from the literature and real firewall configurations of moderate size (≈ 150 rules).
The design and control of the multi-modal locomotion origami robot, Tribot

Origami robots (Robogamis) use architecture to strategically activate different sets and sequence of actuators to achieve large variety of reconfigurable forms. Tribot is a unique mobile origami robot that can simultaneously choose between two modes of locomotion: jumping and crawling. When assembled, Tribot measures 64 × 34 × 20 mm3, weighs 4 g, crawls at 17% of its body length per gait cycle and jumps seven times its height repeatedly without needing to be reset. To optimize the practicality of the nominally 2D design, we made two different approaches to build the prototypes. For one of them, we used the “traditional”, monolithic, layer-by-layer robogami fabrication method and the second, we printed out most parts using a multi-material 3D printer. By showing the performance of two prototypes side-by-side, we show that with the 3D printer, we can minimize the number of functional layers and reduce the fabrication time. The embedded sensors allow Tribot's crawling gait pattern and jumping height to be modulated with a closed loop control. We compare the expected gait step size and displacement to that of the presented prototype while describing the design and control parameters to achieve the experimental results. We also illustrated the preliminary graphical design tool platform developed to optimize the next design iteration of Tribot.
Semantic Granularity in Ontology-Driven Geographic Information Systems

The integration of information of different kinds, such as spatial and alphanumeric at different levels of detail, is a challenge. While a solution is not reached, it is widely recognized that the need to integrate information is so pressing that it does not matter if detail is lost, as long as integration is achieved. This paper shows the potential for information retrieval at different levels of granularity inside the framework of information systems based on ontologies. Ontologies are theories that use a specific vocabulary to describe entities, classes, properties and functions related to a certain view of the world. The use of an ontology, translated into an active information system component, leads to ontology-driven information systems and, in the specific case of GIS, leads to what we call ontology-driven geographic information systems.
Assessment and management of the orthopedic and other complications of Proteus syndrome.

A multidisciplinary workshop was convened at the National Institutes of Health (NIH) to discuss the management of the orthopedic and other complications of Proteus syndrome (PS), a progressive, disproportionate overgrowth disorder. While PS poses many complex challenges, the focus of the workshop was the management of the asymmetric and disorganized skeletal overgrowth that characterizes this multisystem disorder.



Workshop participants developed recommendations for clinical research and patient management and surveillance to maximize the benefits and reduce the risks of surgical and other interventions.



Recommendations for clinical care and management included assessments of skeletal overgrowth and its progression with modalities such as X-ray, magnetic resonance imaging (MRI), dual-energy X-ray absorptiometry, and computerized tomography (CT) imaging. The recommendations also cover the assessment of non-orthopedic complications of PS that significantly impact surgical risk, such as pulmonary embolism and lung bullae. Surgical considerations in PS include assessment of the contribution of contractures to deformities and prophylactic soft-tissue release, aggressive and early use of epiphysiodesis and epiphysiostasis, amputation, and spinal bracing.



Decisions on the timing of orthopedic procedures in children with PS are challenging because they entail balancing the risks of intervention in this high-risk and complex population against the increasing morbidity that patients experience with progressive bony overgrowth. If surgery is delayed too long, the condition may become inoperable. We hope that these recommendations will help clinicians gather appropriate data and assist their patients in making timely treatment decisions.
Measuring and Characterizing Nutritional Information of Food and Ingestion Content in Instagram

Social media sites like Instagram have emerged as popular platforms for sharing ingestion and dining experiences. However research on characterizing the nutritional information embedded in such content is limited. In this paper, we develop a computational method to extract nutritional information, specifically calorific content from Instagram food posts. Next, we explore how the community reacts specifically to healthy versus non-healthy food postings. Based on a crowdsourced approach, our method was found to detect calorific content in posts with 89% accuracy. We further show the use of Instagram as a platform where sharing of moderately healthy food content is common, and such content also receives the most support from the community.
"Exercise to be fit, not skinny": The effect of fitspiration imagery on women's body image.

Fitspiration is an online trend designed to inspire viewers towards a healthier lifestyle by promoting exercise and healthy food. The present study aimed to experimentally investigate the impact of fitspiration images on women's body image. Participants were 130 female undergraduate students who were randomly assigned to view either a set of Instagram fitspiration images or a control set of travel images presented on an iPad. Results showed that acute exposure to fitspiration images led to increased negative mood and body dissatisfaction and decreased state appearance self-esteem relative to travel images. Importantly, regression analyses showed that the effects of image type were mediated by state appearance comparison. Thus it was concluded that fitspiration can have negative unintended consequences for body image. The results offer support to general sociocultural models of media effects on body image, and extend these to "new" media.
Building a motor simulation de novo: Observation of dance by dancers

Research on action simulation identifies brain areas that are active while imagining or performing simple overlearned actions. Are areas engaged during imagined movement sensitive to the amount of actual physical practice? In the present study, participants were expert dancers who learned and rehearsed novel, complex whole-body dance sequences 5 h a week across 5 weeks. Brain activity was recorded weekly by fMRI as dancers observed and imagined performing different movement sequences. Half these sequences were rehearsed and half were unpracticed control movements. After each trial, participants rated how well they could perform the movement. We hypothesized that activity in premotor areas would increase as participants observed and simulated movements that they had learnt outside the scanner. Dancers' ratings of their ability to perform rehearsed sequences, but not the control sequences, increased with training. When dancers observed and simulated another dancer's movements, brain regions classically associated with both action simulation and action observation were active, including inferior parietal lobule, cingulate and supplementary motor areas, ventral premotor cortex, superior temporal sulcus and primary motor cortex. Critically, inferior parietal lobule and ventral premotor activity was modulated as a function of dancers' ratings of their own ability to perform the observed movements and their motor experience. These data demonstrate that a complex motor resonance can be built de novo over 5 weeks of rehearsal. Furthermore, activity in premotor and parietal areas during action simulation is enhanced by the ability to execute a learned action irrespective of stimulus familiarity or semantic label.
ILLINOISCLOUDNLP: Text Analytics Services in the Cloud

Natural Language Processing (NLP) continues to grow in popularity in a range of research and commercial applications. However, installing, maintaining, and running NLP tools can be time consuming, and many commercial and research end users have only intermittent need for large processing capacity. This paper describes ILLINOISCLOUDNLP, an on-demand framework built around NLPCURATOR and Amazon Web Services’ Elastic Compute Cloud (EC2). This framework provides a simple interface to end users via which they can deploy one or more NLPCURATOR instances on EC2, upload plain text documents, specify a set of Text Analytics tools (NLP annotations) to apply, and process and store or download the processed data. It also allows end users to use a model trained on their own data: ILLINOISCLOUDNLP takes care of training, hosting, and applying it to new data just as it does with existing models within NLPCURATOR. As a representative use case, we describe our use of ILLINOISCLOUDNLP to process 3.05 million documents used in the 2012 and 2013 Text Analysis Conference Knowledge Base Population tasks at a relatively deep level of processing, in approximately 20 hours, at an approximate cost of US$500; this is about 20 times faster than doing so on a single server and requires no human supervision and no NLP or Machine Learning expertise.
Hate Source : White Supremacist Hate Groups and Hate Crime

The relationship between hate group activity and hate crime is theoretically ambiguous. Hate groups may incite criminal behavior in support of their beliefs. On the other hand, hate groups may reduce hate crime by serving as a forum for members to verbally vent their frustrations or as protection from future biased violence. I nd that the presence of an active white supremacist hate group chapter is associated with an 18.7 percent higher hate crime rate. White supremacist groups are not associated with the level of anti-white hate crimes committed by non-whites, nor do they form in expectation of future hate crimes by non-whites.
Linking Data to Ontologies

Many organizations nowadays face the problem of accessing existing data sources by means of flexible mechanisms that are both powerful and efficient. Ontologies are widely considered as a suitable formal tool for sophisticated data access. The ontology expresses the domain of interest of the information system at a high level of abstraction, and the relationship between data at the sources and instances of concepts and roles in the ontology is expressed by means of mappings. In this paper we present a solution to the problem of designing effective systems for ontology-based data access. Our solution is based on three main ingredients. First, we present a new ontology language, based on Description Logics, that is particularly suited to reason with large amounts of instances. The second ingredient is a novel mapping language that is able to deal with the so-called impedance mismatch problem, i.e., the problem arising from the difference between the basic elements managed by the sources, namely data, and the elements managed by the ontology, namely objects. The third ingredient is the query answering method, that combines reasoning at the level of the ontology with specific mechanisms for both taking into account the mappings and efficiently accessing the data at the sources.
OpenPAWS: An open source PAWS and UHF TV White Space database implementation for India

TV white space (TVWS) geolocation database is being used for the protection of the terrestrial TV broadcast receivers, and the coexistence of secondary devices. To the best of our knowledge, though TV White Space calculations are available, an active online database does not exist for India. In this paper, the development of the first TVWS database for India is detailed and is released for public access. A standardized protocol to access the TVWS database on a readily available hardware platform is implemented. A hardware prototype, which is capable of querying the TVWS database and operating in the TV band without causing harmful interference to the TV receivers in UHF TV bands, is developed. The source code of our implementation has been released under the GNU general public license version 2.0.
Self-Compassion, Stress, and Coping.

People who are high in self-compassion treat themselves with kindness and concern when they experience negative events. The present article examines the construct of self-compassion from the standpoint of research on coping in an effort to understand the ways in which people who are high in self-compassion cope with stressful events. Self-compassionate people tend to rely heavily on positive cognitive restructuring but do not appear to differ from less self-compassionate people in the degree to which they cope through problem-solving and distraction. Existing evidence does not show clear differences in the degree to which people who are low vs. high in self-compassion seek support as a coping strategy, but more research is needed.
Doing good or doing well ? Image motivation and monetary incentives in behaving prosocially

Doing Good or Doing Well? Image Motivation and Monetary Incentives in Behaving Prosocially This paper experimentally examines image motivation – the desire to be liked and wellregarded by others – as a driver in prosocial behavior (doing good), and asks whether extrinsic monetary incentives (doing well) have a detrimental effect on prosocial behavior due to crowding out of image motivation. By definition, image depends on one’s behavior being visible to other people. Using this unique property we show that image is indeed an important part of the motivation to behave prosocially. Moreover, we show that extrinsic incentives interact with image motivation and are therefore less effective in public than in private. Together, these results imply that image motivation is crowded out by monetary incentives; which in turn means that monetary incentives are more likely to be counterproductive for public prosocial activities than for private ones.
A Simple but Hard-to-Beat Baseline for Session-based Recommendations

Convolutional Neural Networks (CNNs) models have been recently introduced in the domain of top-N session-based recommendations. An ordered collection of past items the user has interacted with in a session (or sequence) are embedded into a 2-dimensional latent matrix, and treated as an image. The convolution and pooling operations are then applied to the mapped item embeddings. In this paper, we first examine the typical session-based CNN recommender and show that both the generative model and network architecture are suboptimal when modeling long-range dependencies in the item sequence. To address the issues, we introduce a simple, but very effective generative model that is capable of learning high-level representation from both shortand long-range item dependencies. The network architecture of the proposed model is formed of a stack of holed convolutional layers, which can efficiently increase the receptive fields without relying on the pooling operation. Another contribution is the effective use of residual block structure in recommender systems, which not only reduces the number of parameters but also eases the optimization for much deeper networks. The proposed generative model attains state-of-the-art accuracy with less training time in the session-based recommendation task. It accordingly can be used as a powerful recommendation baseline to beat in future, especially when there are long sequences of user feedback.
A Position-Biased PageRank Algorithm for Keyphrase Extraction

Given the large amounts of online textual documents available these days, e.g., news articles and scientific papers, effective methods for extracting keyphrases, which provide a highlevel topic description of a document, are greatly needed. We propose PositionRank, an unsupervised graph-based approach to keyphrase extraction that incorporates information from all positions of a word’s occurrences into a biased PageRank to extract keyphrases. Our model obtains remarkable improvements in performance over strong baselines.
TRM-IoT: A trust management model based on fuzzy reputation for internet of things

Since a large scale Wireless Sensor Network (WSN) is to be completely integrated into Internet as a core part of Internet of Things (IoT) or Cyber Physical System (CPS), it is necessary to consider various security challenges that come with IoT/CPS, such as the detection of malicious attacks. Sensors or sensor embedded things may establish direct communication between each other using 6LoWPAN protocol. A trust and reputation model is recognized as an important approach to defend a large distributed sensor networks in IoT/CPS against malicious node attacks, since trust establishment mechanisms can stimulate collaboration among distributed computing and communication entities, facilitate the detection of untrustworthy entities, and assist decision-making process of various protocols. In this paper, based on in-depth understanding of trust establishment process and quantitative comparison among trust establishment methods, we present a trust and reputation model TRM-IoT to enforce the cooperation between things in a network of IoT/CPS based on their behaviors. The accuracy, robustness and lightness of the proposed model is validated through a wide set of simulations.
Better Key Sizes (and Attacks) for LWE-Based Encryption

We analyze the concrete security and key sizes of theoretically sound lattice-based encryption schemes based on the “learning with errors” (LWE) problem. Our main contributions are: (1) a new lattice attack on LWE that combines basis reduction with an enumeration algorithm admitting a time/success tradeoff, which performs better than the simple distinguishing attack considered in prior analyses; (2) concrete parameters and security estimates for an LWE-based cryptosystem that is more compact and efficient than the well-known schemes from the literature. Our new key sizes are up to 10 times smaller than prior examples, while providing even stronger concrete security levels.