nils-herrmann/clean_scirepeval huggingface dataset:
Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data

Use of socially generated "big data" to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society's reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between "real time monitoring" and "early predicting" remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.
Acoustic Echo Cancellation using Adaptive Algorithms

This paper presents the comparison between different adaptive algorithms usages in acoustic echo cancellation. This comparison includes the cancellation of echo generated in room using different adaptive algorithms Least Mean Square (LMS), Normalized Least Mean Square (NLMS), Improved Proportionate Normalized Least Mean Square (IPNLMS) and Recursive Least Squares (RLS) Algorithms. The goal of this work is to choose an optimal algorithm for cancelling acoustic echo noise from the speech signal. There are many adaptive algorithms available in the literature for echo cancellation and every algorithm has its own properties. Our aim is to achieve higher ERLE (amount of echo cancelled) in dB at a higher rate of convergence with low complexity and achieve good amount of SNR (signal to noise ratio). The results verified by using subjective analysis.
Efficient Minimum-Cost Network Hardening Via Exploit Dependency Graphs

In-depth analysis of network security vulnerability must consider attacker exploits not just in isolation, but also in combination. The general approach to this problem is to compute attack paths (combinations of exploits), from which one can decide whether a given set of network hardening measures guarantees the safety of given critical resources. We go beyond attack paths to compute actual sets of hardening measures (assignments of initial network conditions) that guarantee the safety of given critical resources. Moreover, for given costs associated with individual hardening measures, we compute assignments that minimize overall cost. By doing our minimization at the level of initial conditions rather than exploits, we resolve hardening irrelevancies and redundancies in a way that cannot be done through previously proposed exploit-level approaches. Also, we use an efficient exploit-dependency representation based on monotonic logic that has polynomial complexity, as opposed to many previous attack graph representations having exponential complexity.
A review of split-bolus single-pass CT in the assessment of trauma patients

The purpose of this study was to review and compare the image quality and radiation dose of split-bolus single-pass computed tomography(CT) in the assessment of trauma patients in comparison to standard multi-phase CT techniques. An online electronic database was searched using the MESH terms “split-bolus,” “dual phase,” and “single pass.” Inclusion criteria required the research article to compare a split contrast bolus protocol in a single-pass scan in the assessment of trauma patients. Studies using split-bolus CT technique in non-traumatic injury assessment were excluded. Six articles met the inclusion criteria. Parenchymal and vascular image qualities, as well as subjective image quality assessments, were equal or superior in comparison to non-split-bolus multi-phase trauma CT protocols. Split-bolus single-pass CT decreased radiation exposure in all studies. Further research is required to determine the superior split-bolus protocol and the specificity and sensitivity of detecting blunt cerebrovascular injury screening, splenic parenchymal vascular lesions, and characterization of pelvic vascular extravasation.
Lava: Hardware Design in Haskell

Lava is a tool to assist circuit designers in specifying, designing, verifying and implementing hardware. It is a collection of Haskell modules. The system design exploits functional programming language features, such as monads and type classes, to provide multiple interpretations of circuit descriptions. These interpretations implement standard circuit analyses such as simulation, formal verification and the generation of code for the production of real circuits.Lava also uses polymorphism and higher order functions to provide more abstract and general descriptions than are possible in traditional hardware description languages. Two Fast Fourier Transform circuit examples illustrate this.
Securacy: an empirical investigation of Android applications' network usage, privacy and security

Smartphone users do not fully know what their apps do. For example, an applications' network usage and underlying security configuration is invisible to users. In this paper we introduce Securacy, a mobile app that explores users' privacy and security concerns with Android apps. Securacy takes a reactive, personalized approach, highlighting app permission settings that the user has previously stated are concerning, and provides feedback on the use of secure and insecure network communication for each app. We began our design of Securacy by conducting a literature review and in-depth interviews with 30 participants to understand their concerns. We used this knowledge to build Securacy and evaluated its use by another set of 218 anonymous participants who installed the application from the Google Play store. Our results show that access to address book information is by far the biggest privacy concern. Over half (56.4%) of the connections made by apps are insecure, and the destination of the majority of network traffic is North America, regardless of the location of the user. Our app provides unprecedented insight into Android applications' communications behavior globally, indicating that the majority of apps currently use insecure network connections.
Has the athlete trained enough to return to play safely? The acute:chronic workload ratio permits clinicians to quantify a player's risk of subsequent injury.

The return to sport from injury is a difficult multifactorial decision, and risk of reinjury is an important component. Most protocols for ascertaining the return to play status involve assessment of the healing status of the original injury and functional tests which have little proven predictive ability. Little attention has been paid to ascertaining whether an athlete has completed sufficient training to be prepared for competition. Recently, we have completed a series of studies in cricket, rugby league and Australian rules football that have shown that when an athlete's training and playing load for a given week (acute load) spikes above what they have been doing on average over the past 4 weeks (chronic load), they are more likely to be injured. This spike in the acute:chronic workload ratio may be from an unusual week or an ebbing of the athlete's training load over a period of time as in recuperation from injury. Our findings demonstrate a strong predictive (R(2)=0.53) polynomial relationship between acute:chronic workload ratio and injury likelihood. In the elite team setting, it is possible to quantify the loads we are expecting athletes to endure when returning to sport, so assessment of the acute:chronic workload ratio should be included in the return to play decision-making process.
Integrated SDN/NFV management and orchestration architecture for dynamic deployment of virtual SDN control instances for virtual tenant networks [invited]

Software-defined networking (SDN) and network function virtualization (NFV) have emerged as the most promising candidates for improving network function and protocol programmability and dynamic adjustment of network resources. On the one hand, SDN is responsible for providing an abstraction of network resources through well-defined application programming interfaces. This abstraction enables SDN to perform network virtualization, that is, to slice the physical infrastructure and create multiple coexisting application-specific virtual tenant networks (VTNs) with specific quality-of-service and service-levelagreement requirements, independent of the underlying optical transport technology and network protocols. On the other hand, the notion of NFV relates to deploying network functions that are typically deployed in specialized and dedicated hardware, as software instances [called virtual network functions (VNFs)] running on commodity servers (e.g., in data centers) through software virtualization techniques. Despite all the attention that has been given to virtualizing IP functions (e.g., firewall; authentication, authorization, and accounting) or Long-Term Evolution control functions (e.g., mobility management entity, serving gateway, and packet data network gateway), some transport control functions can also be virtualized and moved to the cloud as a VNF. In this work we propose virtualizing the tenant SDN control functions of a VTN and moving them into the cloud. The control of a VTN is a key requirement associated with network virtualization, since it allows the dynamic programming (i.e., direct control and configuration) of the virtual resources allocated to the VTN. We experimentally assess and evaluate the first SDN/NFV orchestration architecture in a multipartner testbed to dynamically deploy independent SDN controller instances for each instantiated VTN and to provide the required connectivity within minutes.
Acquiring the reflectance field of a human face

We present a method to acquire the reflectance field of a human face and use these measurements to render the face under arbitrary changes in lighting and viewpoint. We first acquire images of the face from a small set of viewpoints under a dense sampling of incident illumination directions using a light stage. We then construct a reflectance function image for each observed image pixel from its values over the space of illumination directions. From the reflectance functions, we can directly generate images of the face from the original viewpoints in any form of sampled or computed illumination. To change the viewpoint, we use a model of skin reflectance to estimate the appearance of the reflectance functions for novel viewpoints. We demonstrate the technique with synthetic renderings of a person's face under novel illumination and viewpoints.
Link Prediction in Bipartite Networks-Predicting Yelp Reviews

In this paper, we aim to predict new user reviews on businesses in the Yelp social network. We formulate this as a network link prediction problem by modeling Yelp dataset as a bipartite network between users and businesses. We implement link prediction algorithms with various proximity metrics, thoroughly evaluate the effectiveness of each algorithm and conclude that Delta, AdamicAdar and Common Neighbors algorithms perform the best in precision.
Deep Representation Learning for Human Motion Prediction and Classification

Generative models of 3D human motion are often restricted to a small number of activities and can therefore not generalize well to novel movements or applications. In this work we propose a deep learning framework for human motion capture data that learns a generic representation from a large corpus of motion capture data and generalizes well to new, unseen, motions. Using an encoding-decoding network that learns to predict future 3D poses from the most recent past, we extract a feature representation of human motion. Most work on deep learning for sequence prediction focuses on video and speech. Since skeletal data has a different structure, we present and evaluate different network architectures that make different assumptions about time dependencies and limb correlations. To quantify the learned features, we use the output of different layers for action classification and visualize the receptive fields of the network units. Our method outperforms the recent state of the art in skeletal motion prediction even though these use action specific training data. Our results show that deep feedforward networks, trained from a generic mocap database, can successfully be used for feature extraction from human motion data and that this representation can be used as a foundation for classification and prediction.
ArteFill® Permanent Injectable for Soft Tissue Augmentation: I. Mechanism of Action and Injection Techniques

After more than 25 years of research and development, in October 2006 ArteFill® became the first and only permanent injectable wrinkle filler to receive FDA approval. ArteFill is a third-generation polymeric microsphere-based filler, following its predecessor Artecoll®, which was marketed outside the United States between 1994 and 2006. ArteFill is approved for the correction of nasolabial folds and has been used in over 15,000 patients since its U.S. market introduction in February 2007. No serious side effects have been reported to date according to the FDA’s MAUDE reporting database. ArteFill consists of polymethylmethacrylate (PMMA) microspheres (20% by volume), 30–50 μm in diameter, suspended in 3.5% bovine collagen solution (80% by volume) and 0.3% lidocaine. The collagen carrier is absorbed within 1 month after injection and completely replaced by the patient’s own connective tissue within 3 months. Each cc of ArteFill contains approximately six million microspheres and histological studies have shown that long-term wrinkle correction consists of 80% of the patient’s own connective tissue and 20% microspheres. The standard injection technique is subdermal tunneling that delivers a strand of ArteFill at the dermal–subdermal junction. This strand beneath a wrinkle or fold acts like a support structure that protects against further wrinkling and allows the diminished thickness of the dermis to recover to its original thickness.
Operator and parameter adaptation in genetic algorithms

Genetic Algorithms are a class of powerful, robust search techniques based on genetic inheritance and the Darwinian metaphor of “Natural Selection”. These algorithms maintain a finite memory of individual points on the search landscape known as the “population”. Members of the population are usually represented as strings written over some fixed alphabet, each of which has a scalar value attached to it reflecting its quality or “fitness”. The search may be seen as the iterative application of a number of operators, such as selection, recombination and mutation, to the population with the aim of producing progressively fitter individuals. These operators are usually static, that is to say that their mechanisms, parameters, and probability of application are fixed at the beginning and constant throughout the run of the algorithm. However there is an increasing body of evidence that not only is there no single choice of operators which is optimal for all problems, but that in fact the optimal choice of operators for a given problem will be time-variant i.e. it will depend on such factors as the degree of convergence of the population. Based on theoretical and practical approaches, a number of authors have proposed methods of adaptively controlling one or more of the operators, usually invoking some kind of “meta-learning” algorithm, in order to try and improve the performance of the Genetic Algorithm as a function optimiser.
BodyFusion: Real-Time Capture of Human Motion and Surface Geometry Using a Single Depth Camera

We propose BodyFusion, a novel real-time geometry fusion method that can track and reconstruct non-rigid surface motion of a human performance using a single consumer-grade depth camera. To reduce the ambiguities of the non-rigid deformation parameterization on the surface graph nodes, we take advantage of the internal articulated motion prior for human performance and contribute a skeleton-embedded surface fusion (SSF) method. The key feature of our method is that it jointly solves for both the skeleton and graph-node deformations based on information of the attachments between the skeleton and the graph nodes. The attachments are also updated frame by frame based on the fused surface geometry and the computed deformations. Overall, our method enables increasingly denoised, detailed, and complete surface reconstruction as well as the updating of the skeleton and attachments as the temporal depth frames are fused. Experimental results show that our method exhibits substantially improved nonrigid motion fusion performance and tracking robustness compared with previous state-of-the-art fusion methods. We also contribute a dataset for the quantitative evaluation of fusion-based dynamic scene reconstruction algorithms using a single depth camera.
Investigating Web APIs on the World Wide Web

The world of services on the Web, thus far limited to "classical" Web services based on WSDL and SOAP, has been increasingly marked by the domination of Web APIs, characterised by their relative simplicity and their natural suitability for the Web. Currently, the development of Web APIs is rather autonomous, guided by no established standards or rules, and Web API documentation is commonly not based on an interface description language such as WSDL, but is rather given directly in HTML as part of a web page. As a result, the use of Web APIs requires extensive manual effort and the wealth of existing work on supporting common service tasks, including discovery, composition and invocation, can hardly be reused or adapted to APIs. Before we can achieve a higher level of automation and can make any significant improvement to current practices and technologies, we need to reach a deeper understanding of these. Therefore, in this paper we present a thorough analysis of the current landscape of Web API forms and descriptions, which has up-to-date remained unexplored. We base our findings on manually examining a body of publicly available APIs and, as a result, provide conclusions about common description forms, output types, usage of API parameters, invocation support, level of reusability, API granularity and authentication details. The collected data provides a solid basis for identifying deficiencies and realising how we can overcome existing limitations. More importantly, our analysis can be used as a basis for devising common standards and guidelines for Web API development.
Tracing Linguistic Relations in Winning and Losing Sides of Explicit Opposing Groups

Linguistic relations in oral conversations present how opinions are constructed and developed in a restricted time. The relations bond ideas, arguments, thoughts, and feelings, reshape them during a speech, and finally build knowledge out of all information provided in the conversation. Speakers share a common interest to discuss. It is expected that each speakers reply includes duplicated forms of words from previous speakers. However, linguistic adaptation is observed and evolves in a more complex path than just transferring slightly modified versions of common concepts. A conversation aiming a benefit at the end shows an emergent cooperation inducing the adaptation. Not only cooperation, but also competition drives the adaptation or an opposite scenario and one can capture the dynamic process by tracking how the concepts are linguistically linked. To uncover salient complex dynamic events in verbal communications, we attempt to discover self-organized linguistic relations hidden in a conversation with explicitly stated winners and losers. We examine open access data of the United States Supreme Court. Our understanding is crucial in big data research to guide how transition states in opinion mining and decision-making should be modeled and how this required knowledge to guide the model should be pinpointed, by filtering large amount of data.
Ontology-based sentiment analysis of twitter posts

The emergence of Web 2.0 has drastically altered the way users perceive the Internet, by improving information sharing, collaboration and interoperability. Micro-blogging is one of the most popular Web 2.0 applications and related services, like Twitter, have evolved into a practical means for sharing opinions on almost all aspects of everyday life. Consequently, micro-blogging web sites have since become rich data sources for opinion mining and sentiment analysis. Towards this direction, text-based sentiment classifiers often prove inefficient, since tweets typically do not consist of representative and syntactically consistent words, due to the imposed character limit. This paper proposes the deployment of original ontology-based techniques towards a more efficient sentiment analysis of Twitter posts. The novelty of the proposed approach is that posts are not simply characterized by a sentiment score, as is the case with machine learning-based classifiers, but instead receive a sentiment grade for each distinct notion in the post. Overall, our proposed architecture results in a more detailed analysis of post opinions regarding a specific topic.
Utilizing Anaerobic Fungi for Two-stage Sugar Extraction and Biofuel Production from Lignocellulosic Biomass

Lignocellulosic biomass is a vast and underutilized resource for the production of sugars and biofuels. However, the structural complexity of lignocellulosic biomass and the need for multiple pretreatment and enzymatic steps for sugar release renders this process economically challenging. Here, we report a novel approach for direct, single container, exogenous enzyme-free conversion of lignocellulosic biomass to sugars and biofuels using the anaerobic fungal isolate strain C1A. This approach utilizes simple physiological manipulations for timely inhibition and uncoupling of saccharolytic and fermentative capabilities of strain C1A, leading to the accumulation of sugar monomers (glucose and xylose) in the culture medium. The produced sugars, in addition to fungal hyphal lysate, are subsequently converted by Escherichia coli strain K011 to ethanol. Using this approach, we successfully recovered 17.0% (w/w) of alkali-pretreated corn stover (20.0% of its glucan and xylan content) as sugar monomers in the culture media. More importantly, 14.1% of pretreated corn stover (17.1% of glucan and xylan content) was recovered as ethanol at a final concentration of 28.16 mM after the addition of the ethanologenic strain K011. The high ethanol yield obtained is due to its accumulation as a minor fermentation end product by strain C1A during its initial growth phase, the complete conversion of sugars to ethanol by strain K011, and the possible conversion of unspecified substrates in the hyphal lysate of strain C1A to ethanol by strain K011. This study presents a novel, versatile, and exogenous enzyme-free strategy that utilizes a relatively unexplored group of organisms (anaerobic fungi) for direct biofuel production from lignocellulosic biomass.
Commutation torque ripple reduction in brushless DC motor drives using a single DC current sensor

This paper presents a comprehensive study on reducing commutation torque ripples generated in brushless DC motor drives with only a single DC-link current sensor provided. In such drives, commutation torque ripple suppression techniques that are practically effective in low speed as well as high speed regions are scarcely found. The commutation compensation technique proposed here is based on a strategy that the current slopes of the incoming and the outgoing phases during the commutation interval can be equalized by a proper duty-ratio control. Being directly linked with deadbeat current control scheme, the proposed control method accomplishes suppression of the spikes and dips superimposed on the current and torque responses during the commutation intervals of the inverter. Effectiveness of the proposed control method is verified through simulations and experiments.