nils-herrmann/clean_scirepeval huggingface dataset:
Digital Technologies and the Changing Business Models in Creative Industries

This paper develops a holistic framework to examine the changing business models in creative industries in the digital economy, using evidence gathered from two strands of empirical work. The research found that digital technologies have facilitated pervasive changes in business models in all sectors of the creative industries, but with only a few exceptions, the resultant new business models are often neither novel nor unprecedented. In many cases, digital technologies simply allow organisations to deploy a wider range of business models than previously available to them, often by adapting and applying traditional business models to different ranges or stages of products in the online environment, or simultaneously adopting multiple business models as a portfolio. The paper identifies a series of emerging trends, and highlights three areas for future research.
Age Prediction in Blogs: A Study of Style, Content, and Online Behavior in Pre- and Post-Social Media Generations

Research Assistant May 2008 present Currently working on a project to influence power and rifts in unstructured text project in classifying the age of authors as well as identifying opinion and persuasion in blogs (funded by IARPA SCIL) Previously worked on a project maintaining a pipeline for Question Answering System and creating system tools using Natural Language Processing techniques (funded by DARPA GALE). Computer administrator of the NLP group.
Extraction of Informative Blocks from Web Pages

Typically Web pages always contain a large amount of banner ads, navigation bars, and copyright notices etc. Such irrelevant information is not part of the main contents of the pages, they will seriously harm Web mining and searching. In this paper, we develop and evaluate a method that utilizes both the visual features and the semantic information to extract informative blocks. We first partition a web page into semantic blocks using vision-based page segmentation. The visual and the semantic information got by LSI (Latent Semantic Indexing) are extracted to form the feature-vector of the block. Second we manually annotate informative or uninformative labels to the blocks. The labeled blocks are used as training dataset to train a classification model. Then the informative blocks can be extracted through the model. Our experiments show that the proposed EIBA (Extract Informative Block Arithmetic) is able to dramatically improve the results in near-duplicate detection and classification tasks.
Semantic Matching in Search

Relevance is the most important factor to assure users’ satisfaction in search and the success of a search engine heavily depends on its performance on relevance. It has been observed that most of the dissatisfaction cases in relevance are due to term mismatch between queries and documents (e.g., query “ny times” does not match well with a document only containing “New York Times”), because term matching, i.e., the bag-of-words approach, still functions as the main mechanism of modern search engines. It is not exaggerated to say, therefore, that mismatch between query and document poses the most critical challenge in search. Ideally, one would like to see query and document match with each other, if they are topically relevant. Recently, researchers have expended significant effort to address the problem. The major approach is to conduct semantic matching, i.e., to perform more query and document understanding to represent the meanings of them, and perform better matching between the enriched query and document representations. With the availability of large amounts of log data and advanced machine learning techniques, this becomes more feasible and significant progress has been made recently. This survey gives a systematic and detailed introduction to newly developed machine learning technologies for query document matching (semantic matching) in search, particularly web search. It focuses on the fundamental problems, as well as the state-of-the-art solutions of query document matching on form aspect, phrase aspect, word sense aspect, topic aspect, and structure aspect. The ideas and solutions explained may motivate industrial practitioners to turn the research results into products. The methods introduced and the discussions made may also stimulate academic researchers to find new research directions and approaches. Matching between query and document is not limited to search and similar problems can be found in question answering, online advertising, cross-language information retrieval, machine translation, recommender systems, link prediction, image annotation, drug design, and other applications, as the general task of matching between objects from two different spaces. The technologies Full text available at: http://dx.doi.org/10.1561/1500000035
Inverse Rendering for Computer Graphics All Rights Reserved Inverse Rendering for Computer Graphics

Creating realistic images has been a major focus in the study of computer graphics for much of its history. This eeort has led to mathematical models and algorithms that can compute predictive, or physically realistic, images from known camera positions and scene descriptions that include the geometry of objects, the reeectance of surfaces, and the lighting used to illuminate the scene. These images accurately describe the physical quantities that would be measured from a real scene. Because these algorithms can predict real images, they can also be used in inverse problems to work backward from photographs to attributes of the scene. Work on three such inverse rendering problems is described. The rst, inverse lighting, assumes knowledge of geometry, reeectance, and the recorded photograph and solves for the lighting in the scene. A technique using a linear least-squares system is proposed and demonstrated. Also demonstrated is an application of inverse lighting, called re-lighting, which modiies lighting in photographs. The second two i n verse rendering problems solve for unknown reeectance, given images with known geometry, lighting, and camera positions. Photographic texture measurement concentrates on capturing the spatial variation in an object's reeectance. The resulting system begins with scanned 3D models of real objects and uses photographs to construct accurate, high-resolution textures suitable for physically realistic rendering. The system is demonstrated on two complex natural objects with detailed surface textures. Image-based BRDF measurement takes the opposite approach to reeectance measurement , capturing the directional characteristics of a surface's reeectance by measuring the bidirectional reeectance distribution function, or BRDF. Using photographs of an object with spatially uniform reeectance, the BRDFs of paints and papers are measured with completeness and accuracy that rival that of measurements obtained using specialized devices. The image-based approach and novel light source positioning technique require only general-purpose equipment, so the cost of the apparatus is low compared to conventional approaches. In addition, very densely sampled data can bemeasured very quickly, when the wavelength spectrum of the BRDF does not need to be measured in detail. iii for Heidi iv Acknowledgements My rst thanks go to my advisor, Don Greenberg, for the support and encouragement he gave me during my stay at Cornell. His constant conndence in me and my work has been an inspiration, and he has surrounded me with the people and technology that made that work possible. I w ould also like to thank Len Gross and …
Towards Building Forensics Enabled Cloud Through Secure Logging-as-a-Service

Collection and analysis of various logs (e.g., process logs, network logs) are fundamental activities in computer forensics. Ensuring the security of the activity logs is therefore crucial to ensure reliable forensics investigations. However, because of the black-box nature of clouds and the volatility and co-mingling of cloud data, providing the cloud logs to investigators while preserving users' privacy and the integrity of logs is challenging. The current secure logging schemes, which consider the logger as trusted cannot be applied in clouds since there is a chance that cloud providers (logger) collude with malicious users or investigators to alter the logs. In this paper, we analyze the threats on cloud users' activity logs considering the collusion between cloud users, providers, and investigators. Based on the threat model, we propose Secure-Logging-as-a-Service ( SecLaaS), which preserves various logs generated for the activity of virtual machines running in clouds and ensures the confidentiality and integrity of such logs. Investigators or the court authority can only access these logs by the RESTful APIs provided by SecLaaS, which ensures confidentiality of logs. The integrity of the logs is ensured by hash-chain scheme and proofs of past logs published periodically by the cloud providers. In prior research, we used two accumulator schemes Bloom filter and RSA accumulator to build the proofs of past logs. In this paper, we propose a new accumulator scheme - Bloom-Tree, which performs better than the other two accumulators in terms of time and space requirement.
VERSE: Versatile Graph Embeddings from Similarity Measures

Embedding aweb-scale information network into a low-dimensional vector space facilitates tasks such as link prediction, classification, and visualization. Past research has addressed the problem of extracting such embeddings by adopting methods from words to graphs, without defining a clearly comprehensible graph-related objective. Yet, as we show, the objectives used in past works implicitly utilize similarity measures among graph nodes. In this paper, we carry the similarity orientation of previous works to its logical conclusion; we propose VERtex Similarity Embeddings (VERSE), a simple, versatile, and memory-efficient method that derives graph embeddings explicitly calibrated to preserve the distributions of a selected vertex-to-vertex similarity measure. VERSE learns such embeddings by training a single-layer neural network. While its default, scalable version does so via sampling similarity information, we also develop a variant using the full information per vertex. Our experimental study on standard benchmarks and real-world datasets demonstrates that VERSE, instantiated with diverse similarity measures, outperforms state-of-the-art methods in terms of precision and recall in major data mining tasks and supersedes them in time and space efficiency, while the scalable sampling-based variant achieves equally good results as the nonscalable full variant.  Anton Tsitsulin, Davide Mottin, Panagiotis Karras, and Emmanuel Müller.  Versatile Graph Embeddings from Similarity Measures. In WWW 2018: The 2018 Web Conference, April 23–27, 2018, Lyon, France. ACM, New York, NY, USA, 10 pages.
A randomized controlled trial of postcrisis suicide prevention.

This study tested the hypothesis that professionals' maintenance of long-term contact with persons who are at risk of suicide can exert a suicide-prevention influence. This influence was hypothesized to result from the development of a feeling of connectedness and to be most pertinent to high-risk individuals who refuse to remain in the health care system.



A total of 3,005 persons hospitalized because of a depressive or suicidal state, populations known to be at risk of subsequent suicide, were contacted 30 days after discharge about follow-up treatment. A total of 843 patients who had refused ongoing care were randomly divided into two groups; persons in one group were contacted by letter at least four times a year for five years. The other group-the control group-received no further contact. A follow-up procedure identified patients who died during the five-year contact period and during the subsequent ten years. Suicide rates in the contact and no-contact groups were compared.



Patients in the contact group had a lower suicide rate in all five years of the study. Formal survival analyses revealed a significantly lower rate in the contact group (p=.04) for the first two years; differences in the rates gradually diminished, and by year 14 no differences between groups were observed.



A systematic program of contact with persons who are at risk of suicide and who refuse to remain in the health care system appears to exert a significant preventive influence for at least two years. Diminution of the frequency of contact and discontinuation of contact appear to reduce and eventually eliminate this preventive influence.
Hunger and satiety in anorexia nervosa: fMRI during cognitive processing of food pictures

Neuroimaging studies of visually presented food stimuli in patients with anorexia nervosa have demonstrated decreased activations in inferior parietal and visual occipital areas, and increased frontal activations relative to healthy persons, but so far no inferences could be drawn with respect to the influence of hunger or satiety. Thirteen patients with AN and 10 healthy control subjects (aged 13-21) rated visual food and non-food stimuli for pleasantness during functional magnetic resonance imaging (fMRI) in a hungry and a satiated state. AN patients rated food as less pleasant than controls. When satiated, AN patients showed decreased activation in left inferior parietal cortex relative to controls. When hungry, AN patients displayed weaker activation of the right visual occipital cortex than healthy controls. Food stimuli during satiety compared with hunger were associated with stronger right occipital activation in patients and with stronger activation in left lateral orbitofrontal cortex, the middle portion of the right anterior cingulate, and left middle temporal gyrus in controls. The observed group differences in the fMRI activation to food pictures point to decreased food-related somatosensory processing in AN during satiety and to attentional mechanisms during hunger that might facilitate restricted eating in AN.
An RIG-I-Like RNA Helicase Mediates Antiviral RNAi Downstream of Viral siRNA Biogenesis in Caenorhabditis elegans

Dicer ribonucleases of plants and invertebrate animals including Caenorhabditis elegans recognize and process a viral RNA trigger into virus-derived small interfering RNAs (siRNAs) to guide specific viral immunity by Argonaute-dependent RNA interference (RNAi). C. elegans also encodes three Dicer-related helicase (drh) genes closely related to the RIG-I-like RNA helicase receptors which initiate broad-spectrum innate immunity against RNA viruses in mammals. Here we developed a transgenic C. elegans strain that expressed intense green fluorescence from a chromosomally integrated flock house virus replicon only after knockdown or knockout of a gene required for antiviral RNAi. Use of the reporter nematode strain in a feeding RNAi screen identified drh-1 as an essential component of the antiviral RNAi pathway. However, RNAi induced by either exogenous dsRNA or the viral replicon was enhanced in drh-2 mutant nematodes, whereas exogenous RNAi was essentially unaltered in drh-1 mutant nematodes, indicating that exogenous and antiviral RNAi pathways are genetically distinct. Genetic epistatic analysis shows that drh-1 acts downstream of virus sensing and viral siRNA biogenesis to mediate specific antiviral RNAi. Notably, we found that two members of the substantially expanded subfamily of Argonautes specific to C. elegans control parallel antiviral RNAi pathways. These findings demonstrate both conserved and unique strategies of C. elegans in antiviral defense.
Processing Set Expressions over Continuous Update Streams

There is growing interest in algorithms for processing and querying continuous data streams (i.e., data that is seen only once in a fixed order) with limited memory resources. In its most general form, a data stream is actually an update stream, i.e., comprising data-item deletions as well as insertions. Such massive update streams arise naturally in several application domains (e.g., monitoring of large IP network installations, or processing of retail-chain transactions).Estimating the cardinality of set expressions defined over several (perhaps, distributed) update streams is perhaps one of the most fundamental query classes of interest; as an example, such a query may ask "what is the number of distinct IP source addresses seen in passing packets from both router R1 and R2 but not router R3?". Earlier work has only addressed very restricted forms of this problem, focusing solely on the special case of insert-only streams and specific operators (e.g., union). In this paper, we propose the first space-efficient algorithmic solution for estimating the cardinality of full-fledged set expressions over general update streams. Our estimation algorithms are probabilistic in nature and rely on a novel, hash-based synopsis data structure, termed "2-level hash sketch". We demonstrate how our 2-level hash sketch synopses can be used to provide low-error, high-confidence estimates for the cardinality of set expressions (including operators such as set union, intersection, and difference) over continuous update streams, using only small space and small processing time per update. Furthermore, our estimators never require rescanning or resampling of past stream items, regardless of the number of deletions in the stream. We also present lower bounds for the problem, demonstrating that the space usage of our estimation algorithms is within small factors of the optimal. Preliminary experimental results verify the effectiveness of our approach.
Context-Aware Mixed Reality: A Framework for Ubiquitous Interaction

Mixed Reality (MR) is a powerful interactive technology that yields new types of user experience. We present a semantic based interactive MR framework that exceeds the current geometry level approaches, a step change in generating high-level context-aware interactions. Our key insight is to build semantic understanding in MR that not only can greatly enhance user experience through object-specific behaviours, but also pave the way for solving complex interaction design challenges. The framework generates semantic properties of the real world environment through dense scene reconstruction and deep image understanding. We demonstrate our approach with a material-aware prototype system for generating context-aware physical interactions between the real and the virtual objects. Quantitative and qualitative evaluations are carried out and the results show that the framework delivers accurate and fast semantic information in interactive MR environment, providing effective semantic level interactions.
Prefetching-Based Data Dissemination in Vehicular Cloud Systems

In the last decade, vehicular ad hoc networks (VANETs) have been widely studied as an effective method for providing wireless communication connectivity in vehicular transportation systems. In particular, vehicular cloud systems (VCSs) have received abundant interest for the ability to offer a variety of vehicle information services. We consider the data dissemination problem of providing reliable data delivery services from a cloud data center to vehicles through roadside wireless access points (APs) with local data storage. Due to intermittent wireless connectivity and the limited data storage size of roadside wireless APs, the question of how to use the limited resources of the wireless APs is one of the most pressing issues affecting data dissemination efficiency in VCSs. In this paper, we devise a vehicle route-based data prefetching scheme, which maximizes data dissemination success probability in an average sense when the size of local data storage is limited and wireless connectivity is stochastically unknown. We propose a greedy algorithm and an online learning algorithm for deterministic and stochastic cases, respectively, to decide how to prefetch a set of data of interest from a data center to roadside wireless APs. Experiment results indicate that the proposed algorithms can achieve efficient data dissemination in a variety of vehicular scenarios.
DIRECT GEOMETRY PROCESSING FOR TELE-FABRICATION

This paper presents a new approach for tele-fabrication where a physical object is scanned in one location and fabricated in another location. This approach integrates threedimensional (3D) scanning, geometric processing of scanned data, and additive manufacturing technologies. In this paper, we focus on a set of direct geometric processing techniques that enable the tele-fabrication. In this approach, 3D scan data is directly sliced into layer-wise contours. Sacrificial supports are generated directly from the contours and digital mask images of the objects and the supports for Stereolithography Apparatus (SLA) processes are then automatically generated. The salient feature of this approach is that it does not involve any intermediate geometric models such as STL, polygons or nonuniform rational B-splines that are otherwise commonly used in prevalent approaches. The experimental results on a set of objects fabricated on several SLA machines confirm the effectiveness of the approach in faithfully tele-fabricating physical objects.
NCDawareRank: a novel ranking method that exploits the decomposable structure of the web

Research about the topological characteristics of the hyperlink graph has shown that Web possesses a nested block structure, indicative of its innate hierarchical organization. This crucial observation opens the way for new approaches that can usefully regard Web as a Nearly Completely Decomposable(NCD) system; In recent years, such approaches gave birth to various efficient methods and algorithms that exploit NCD from a computational point of view and manage to considerably accelerate the extraction of the PageRank vector. However, very little have been done towards the qualitative exploitation of NCD.
 In this paper we propose NCDawareRank, a novel ranking method that uses the intuition behind NCD to generalize and refine PageRank. NCDawareRank considers both the link structure and the hierarchical nature of the Web in a way that preserves the mathematically attractive characteristics of PageRank and at the same time manages to successfully resolve many of its known problems, including Web Spamming Susceptibility and Biased Ranking of Newly Emerging Pages. Experimental results show that NCDawareRank is more resistant to direct manipulation, alleviates the problems caused by the sparseness of the link graph and assigns more reasonable ranking scores to newly added pages, while maintaining the ability to be easily implemented on a large-scale and in a computationally efficient manner.
A Low Complexity Antenna Switching for Joint Wireless Information and Energy Transfer in MIMO Relay Channels

In this paper, we investigate a low-complexity technique for simultaneous wireless information and energy transfer in multiple-input multiple-output relay channels. The proposed technique exploits the array configuration at the relay node and uses the antenna elements either for conventional decoding or for rectifying (rectennas). In order to keep the complexity low, a dynamic antenna switching between decoding/rectifying is proposed based on the principles of the generalized selection combiner (GSC); the L strongest paths are allocated for decoding while the remaining channel paths for rectifying (and vice versa). The optimal L as well as the allocation strategy that minimizes the outage probability are investigated via theoretical and numerical results. In addition, two performance bounds that provide the optimal performance without the limitation of GSC are proposed by solving a linear programming and a binary knapsack problem, respectively. The proposed technique is extended to scenarios with multi-user interference, where a zero-forcing receiver is used at the relay node; closed-forms expressions for the outage probability are also derived.