nils-herrmann/clean_scirepeval huggingface dataset:
An empirical study of low-power wireless

We present empirical measurements of the packet delivery performance of the latest sensor platforms: Micaz and Telos motes. In this article, we present observations that have implications to a set of common assumptions protocol designers make while designing sensornet protocols—specifically—the MAC and network layer protocols. We first distill these common assumptions in to a conceptual model and show how our observations support or dispute these assumptions. We also present case studies of protocols that do not make these assumptions. Understanding the implications of these observations to the conceptual model can improve future protocol designs.
Unified Point-of-Interest Recommendation with Temporal Interval Assessment

Point-of-interest (POI) recommendation, which helps mobile users explore new places, has become an important location-based service. Existing approaches for POI recommendation have been mainly focused on exploiting the information about user preferences, social influence, and geographical influence. However, these approaches cannot handle the scenario where users are expecting to have POI recommendation for a specific time period. To this end, in this paper, we propose a unified recommender system, named the 'Where and When to gO' (WWO) recommender system, to integrate the user interests and their evolving sequential preferences with temporal interval assessment. As a result, the WWO system can make recommendations dynamically for a specific time period and the traditional POI recommender system can be treated as the special case of the WWO system by setting this time period long enough. Specifically, to quantify users' sequential preferences, we consider the distributions of the temporal intervals between dependent POIs in the historical check-in sequences. Then, to estimate the distributions with only sparse observations, we develop the low-rank graph construction model, which identifies a set of bi-weighted graph bases so as to learn the static user preferences and the dynamic sequential preferences in a coherent way. Finally, we evaluate the proposed approach using real-world data sets from several location-based social networks (LBSNs). The experimental results show that our method outperforms the state-of-the-art approaches for POI recommendation in terms of various metrics, such as F-measure and NDCG, with a significant margin.
Bayesian Modeling of Facial Similarity

In previous work, we advanced a new technique for direct visual matching of images for the purposes of face recognition and image retrieval, using a probabilistic measure of similarity based primarily on a Bayesian (MAP) analysis of image differences, leading to a d̈ual̈basis similar to eigenfaces. The performance advantage of this probabilistic matching technique over standard Euclidean nearest-neighbor eigenface matching was recently demonstrated using results from DARPAś 1996F̈ERET̈face recognition competition, in which this probabilistic matching algorithm was found to be the top performer. We have further developed a simple method of replacing the costly compution of nonlinear (online) Bayesian similarity measures by the relatively inexpensive computation of linear (offline) subspace projections and simple (online) Euclidean norms, thus resulting in a significant computational speed-up for implementation with very large image databases as typically encountered in real-world applications. Advances in Neural Information Processing Systems 11, M. S. Kearns, S. A. Solla, D. A. Cohn, (Eds.), MIT Press, 1999. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved.
Principled Sampling for Anomaly Detection

Anomaly detection plays an important role in protecting computer systems from unforeseen attack by automatically recognizing and filter atypical inputs. However, it can be difficult to balance the sensitivity of a detector – an aggressive system can filter too many benign inputs while a conservative system can fail to catch anomalies. Accordingly, it is important to rigorously test anomaly detectors to evaluate potential error rates before deployment. However, principled systems for doing so have not been studied – testing is typically ad hoc, making it difficult to reproduce results or formally compare detectors. To address this issue we present a technique and implemented system, Fortuna, for obtaining probabilistic bounds on false positive rates for anomaly detectors that process Internet data. Using a probability distribution based on PageRank and an efficient algorithm to draw samples from the distribution, Fortuna computes an estimated false positive rate and a probabilistic bound on the estimate’s accuracy. By drawing test samples from a well defined distribution that correlates well with data seen in practice, Fortuna improves on ad hoc methods for estimating false positive rate, giving bounds that are reproducible, comparable across different anomaly detectors, and theoretically sound. Experimental evaluations of three anomaly detectors (SIFT, SOAP, and JSAND) show that Fortuna is efficient enough to use in practice — it can sample enough inputs to obtain tight false positive rate bounds in less than 10 hours for all three detectors. These results indicate that Fortuna can, in practice, help place anomaly detection on a stronger theoretical foundation and help practitioners better understand the behavior and consequences of the anomaly detectors that they deploy. As part of our work, we obtain a theoretical result that may be of independent interest: We give a simple analysis of the convergence rate of the random surfer process defining PageRank that guarantees the same rate as the standard, second-eigenvalue analysis, but does not rely on any assumptions about the link structure of the web.
Locality-constrained Linear Coding for image classification

The traditional SPM approach based on bag-of-features (BoF) requires nonlinear classifiers to achieve good image classification performance. This paper presents a simple but effective coding scheme called Locality-constrained Linear Coding (LLC) in place of the VQ coding in traditional SPM. LLC utilizes the locality constraints to project each descriptor into its local-coordinate system, and the projected coordinates are integrated by max pooling to generate the final representation. With linear classifier, the proposed approach performs remarkably better than the traditional nonlinear SPM, achieving state-of-the-art performance on several benchmarks. Compared with the sparse coding strategy [22], the objective function used by LLC has an analytical solution. In addition, the paper proposes a fast approximated LLC method by first performing a K-nearest-neighbor search and then solving a constrained least square fitting problem, bearing computational complexity of O(M + K2). Hence even with very large codebooks, our system can still process multiple frames per second. This efficiency significantly adds to the practical values of LLC for real applications.
Lumitrack: low cost, high precision, high speed tracking with projected m-sequences

We present Lumitrack, a novel motion tracking technology that uses projected structured patterns and linear optical sensors. Each sensor unit is capable of recovering 2D location within the projection area, while multiple sensors can be combined for up to six degree of freedom (DOF) tracking. Our structured light approach is based on special patterns, called m-sequences, in which any consecutive sub-sequence of m bits is unique. Lumitrack can utilize both digital and static projectors, as well as scalable embedded sensing configurations. The resulting system enables high-speed, high precision, and low-cost motion tracking for a wide range of interactive applications. We detail the hardware, operation, and performance characteristics of our approach, as well as a series of example applications that highlight its immediate feasibility and utility.
Global Optimality in Neural Network Training

The past few years have seen a dramatic increase in the performance of recognition systems thanks to the introduction of deep networks for representation learning. However, the mathematical reasons for this success remain elusive. A key issue is that the neural network training problem is nonconvex, hence optimization algorithms may not return a global minima. This paper provides sufficient conditions to guarantee that local minima are globally optimal and that a local descent strategy can reach a global minima from any initialization. Our conditions require both the network output and the regularization to be positively homogeneous functions of the network parameters, with the regularization being designed to control the network size. Our results apply to networks with one hidden layer, where size is measured by the number of neurons in the hidden layer, and multiple deep subnetworks connected in parallel, where size is measured by the number of subnetworks.
One-Class Classification with Extreme Learning Machine

One-class classification problemhas been investigated thoroughly for past decades. Among one of themost effective neural network approaches for one-class classification, autoencoder has been successfully applied for many applications. However, this classifier relies on traditional learning algorithms such as backpropagation to train the network, which is quite time-consuming. To tackle the slow learning speed in autoencoder neural network, we propose a simple and efficient one-class classifier based on extreme learning machine (ELM).The essence of ELM is that the hidden layer need not be tuned and the output weights can be analytically determined, which leads to much faster learning speed.The experimental evaluation conducted on several real-world benchmarks shows that the ELM based one-class classifier can learn hundreds of times faster than autoencoder and it is competitive over a variety of one-class classification methods.
Local Fisher discriminant analysis for supervised dimensionality reduction

Dimensionality reduction is one of the important preprocessing steps in high-dimensional data analysis. In this paper, we consider the supervised dimensionality reduction problem where samples are accompanied with class labels. Traditional Fisher discriminant analysis is a popular and powerful method for this purpose. However, it tends to give undesired results if samples in some class form several separate clusters, i.e., multimodal. In this paper, we propose a new dimensionality reduction method called local Fisher discriminant analysis (LFDA), which is a localized variant of Fisher discriminant analysis. LFDA takes local structure of the data into account so the multimodal data can be embedded appropriately. We also show that LFDA can be extended to non-linear dimensionality reduction scenarios by the kernel trick.
Influencing users towards better passwords: persuasive cued click-points

Usable security has unique usability challenges bec ause the need for security often means that standard human-comput er-in eraction approaches cannot be directly applied. An important usability goal for authentication systems is to support users in s electing better passwords, thus increasing security by expanding th e effective password space. In click-based graphical passwords, poorly chosen passwords lead to the emergence of hotspots – portions of the image where users are more likely to select cli ck-points, allowing attackers to mount more successful diction ary attacks. We use persuasion to influence user choice in click -based graphical passwords, encouraging users to select mo re random, and hence more secure, click-points. Our approach i s to introduce persuasion to the Cued Click-Points graphical passw ord scheme (Chiasson, van Oorschot, Biddle, 2007) . Our resulting scheme significantly reduces hotspots while still maintain ing its usability.
Role of hope in academic and sport achievement.

Hope is the sum of goal thoughts as tapped by pathways and agency. Pathways reflect the perceived capability to produce goal routes; agency reflects the perception that one can initiate action along these pathways. Using trait and state hope scales, studies explored hope in college student athletes. In Study 1, male and female athletes were higher in trait hope than nonathletes; moreover, hope significantly predicted semester grade averages beyond cumulative grade point average and overall self-worth. In Study 2, with female cross-country athletes, trait hope predicted athletic outcomes; further, weekly state hope tended to predict athletic outcomes beyond dispositional hope, training, and self-esteem, confidence, and mood. In Study 3, with female track athletes, dispositional hope significantly predicted athletic outcomes beyond variance related to athletic abilities and affectivity; moreover, athletes had higher hope than nonathletes.
ReFlex: Remote Flash ≈ Local Flash

Remote access to NVMe Flash enables flexible scaling and high utilization of Flash capacity and IOPS within a datacenter. However, existing systems for remote Flash access either introduce significant performance overheads or fail to isolate the multiple remote clients sharing each Flash device. We present ReFlex, a software-based system for remote Flash access, that provides nearly identical performance to accessing local Flash. ReFlex uses a dataplane kernel to closely integrate networking and storage processing to achieve low latency and high throughput at low resource requirements. Specifically, ReFlex can serve up to 850K IOPS per core over TCP/IP networking, while adding 21us over direct access to local Flash. ReFlex uses a QoS scheduler that can enforce tail latency and throughput service-level objectives (SLOs) for thousands of remote clients. We show that ReFlex allows applications to use remote Flash while maintaining their original performance with local Flash.
Fairness via source throttling: a configurable and high-performance fairness substrate for multi-core memory systems

Cores in a chip-multiprocessor (CMP) system share multiple hardware resources in the memory subsystem. If resource sharing is unfair, some applications can be delayed significantly while others are unfairly prioritized. Previous research proposed separate fairness mechanisms in each individual resource. Such resource-based fairness mechanisms implemented independently in each resource can make contradictory decisions, leading to low fairness and loss of performance. Therefore, a coordinated mechanism that provides fairness in the entire shared memory system is desirable.
 This paper proposes a new approach that provides fairness in the entire shared memory system, thereby eliminating the need for and complexity of developing fairness mechanisms for each individual resource. Our technique, Fairness via Source Throttling (FST), estimates the unfairness in the entire shared memory system. If the estimated unfairness is above a threshold set by system software, FST throttles down cores causing unfairness by limiting the number of requests they can inject into the system and the frequency at which they do. As such, our source-based fairness control ensures fairness decisions are made in tandem in the entire memory system. FST also enforces thread priorities/weights, and enables system software to enforce different fairness objectives and fairness-performance tradeoffs in the memory system.
 Our evaluations show that FST provides the best system fairness and performance compared to four systems with no fairness control and with state-of-the-art fairness mechanisms implemented in both shared caches and memory controllers.
A visualization tool for evaluating access control policies in facebook-style social network systems

Understanding the privacy implication of adopting a certain privacy setting is a complex task for the users of social network systems. Users need tool support to articulate potential access scenarios and perform policy analysis. Such a need is particularly acute for Facebook-style Social Network Systems (FSNSs), in which semantically rich topology-based policies are used for access control. In this work, we develop a prototypical tool for Reflective Policy Assessment (RPA) --- a process in which a user examines her profile from the viewpoint of another user in her extended neighbourhood in the social graph. We verify the utility and usability of our tool in a within-subject user study.
The Use of WordNet in Information Retrieval.

WordNet has been used in information retrieval research by many researchers, but failed to improve the performance of their retrieval system. Thereby in this paper we investigate why the use of WordNet has not been successful. Based on this analysis we propose a method of making WordNet more useful in information retrieval applications. Experiments using several standard information retrieval test collections show that our method results in a significant improvement of information retrieval performance.
Parkinson's disease: clinical features and diagnosis.

Parkinson's disease (PD) is a progressive neurological disorder characterised by a large number of motor and non-motor features that can impact on function to a variable degree. This review describes the clinical characteristics of PD with emphasis on those features that differentiate the disease from other parkinsonian disorders.



A MedLine search was performed to identify studies that assess the clinical characteristics of PD. Search terms included "Parkinson's disease", "diagnosis" and "signs and symptoms".



Because there is no definitive test for the diagnosis of PD, the disease must be diagnosed based on clinical criteria. Rest tremor, bradykinesia, rigidity and loss of postural reflexes are generally considered the cardinal signs of PD. The presence and specific presentation of these features are used to differentiate PD from related parkinsonian disorders. Other clinical features include secondary motor symptoms (eg, hypomimia, dysarthria, dysphagia, sialorrhoea, micrographia, shuffling gait, festination, freezing, dystonia, glabellar reflexes), non-motor symptoms (eg, autonomic dysfunction, cognitive/neurobehavioral abnormalities, sleep disorders and sensory abnormalities such as anosmia, paresthesias and pain). Absence of rest tremor, early occurrence of gait difficulty, postural instability, dementia, hallucinations, and the presence of dysautonomia, ophthalmoparesis, ataxia and other atypical features, coupled with poor or no response to levodopa, suggest diagnoses other than PD.



A thorough understanding of the broad spectrum of clinical manifestations of PD is essential to the proper diagnosis of the disease. Genetic mutations or variants, neuroimaging abnormalities and other tests are potential biomarkers that may improve diagnosis and allow the identification of persons at risk.
Better with bubbles: enhancing the visual realism of simulated fluid

We present a method for including the visual effect of bubbles in a computer graphics fluid simulation, thus enhancing the illusion of realism for a splashing fluid. Previous fluid simulation methods have not included bubbles. Bubble creation is integrated into the particle level-set fluid simulation algorithm. Individual bubbles are approximated by spheres, which form more complex shapes where they intersect. The rendering of bubbles and fluid are integrated to create the appearance of one continuous surface. At the fluid-air boundary, we integrate bubbles whenever level-set marker particles pass from from the outside to the inside of the fluid. Thus, these particles represent air that has become trapped within the fluid surface. In addition, we detect empty pockets within the fluid, that are often formed due to turbulence, and create bubbles within this space. This is an inexpensive way of giving the impression that the air trapped in air pockets has become bubbles. Photo-realistic images of simulation results are rendered with a raytracer that has been enhanced to include caustics, and to handle bubble-bubble interfaces. Comparison of these images with images rendered without bubbles supports our position that the simple addition of bubbles to a fluid simulation greatly enhances visual realism.
Is the internet a better public sphere? Comparing old and new media in the USA and Germany

Normative theorists of the public sphere, such as Jürgen Habermas, have been very critical of the ‘old’ mass media, which were seen as unable to promote free and plural societal communication. The advent of the internet, in contrast, gave rise to hopes that it would make previously marginalized actors and arguments more visible to a broader public. To assess these claims, this article compares the internet and mass media communication. It distinguishes three levels of both the offline and the online public sphere, which differ in their structural prerequisites, in their openness for participation and in their influence on the wider society. Using this model, the article compares the levels that are most strongly structured and most influential for the wider society: the mass media and communication as organized by search engines. Using human genome research and analysing Germany and the USA, the study looks at which actors, evaluations and frames are present in the print mass media and on websites, and finds that internet communication does not differ significantly from the offline debate in the print media.
Dynamics between the trust transfer process and intention to use mobile payment services: A cross-environment perspective

Many Internet-based services have already been ported to the mobile-based environment, embracing the new services is therefore critical to deriving revenue for services providers. Based on a valence framework and trust transfer theory, we developed a trust-based customer decision-making model of the non-independent, third-party mobile payment services context. We empirically investigated whether a customer’s established trust in Internet payment services is likely to influence his or her initial trust in mobile payment services. We also examined how these trust beliefs might interact with both positive and negative valence factors and affect a customer’s adoption of mobile payment services. Our SEM analysis indicated that trust indeed had a substantial impact on the cross-environment relationship and, further, that trust in combination with the positive and negative valence determinants directly and indirectly influenced behavioral intention. In addition, the magnitudes of these effects on workers and students were significantly different from each other.