nils-herrmann/clean_scirepeval huggingface dataset:
An Optimal Auction Mechanism for Mobile Edge Caching

With the explosive growth of wireless data, mobile edge caching has emerged as a promising paradigm to support mobile traffic recently, in which the service providers (SPs) prefetch some popular contents in advance and cache them locally at the network edge. When requested, those locally cached contents can be directly delivered to users with low latency, thus alleviating the traffic load over backhaul channels during peak hours and enhancing the quality-of-experience (QoE) of users simultaneously. Due to the limited available cache space, it makes sense for the SP to cache the most profitable contents. Nevertheless, users' true valuations of contents are their private knowledge, which is unknown to the SP in general. This information asymmetry poses a significant challenge for effective caching at the SP side. Further, the cached contents can be delivered with different quality, which needs to be chosen judiciously to balance delivery costs and user satisfaction. To tackle these difficulties, in this paper, we propose an optimal auction mechanism from the perspective of the SP. In the auction, the SP determines the cache space allocation over contents and user payments based on the users' (possibly untruthful) reports of their valuations so that the SP's expected revenue is maximized. The advocated mechanism is designed to elicit true valuations from the users (incentive compatibility) and to incentivize user participation (individual rationality). In addition, we devise a computationally efficient method for calculating the optimal cache space allocation and user payments. We further examine the optimal choice of the content delivery quality for the case with a large number of users and derive a closed-form solution to compute the optimal delivery quality. Finally, extensive simulations are implemented to evaluate the performance of the proposed optimal auction mechanism, and the impact of various model parameters is highlighted to obtain engineering insights into the content caching problem.
Pacific kids DASH for health (PacDASH) randomized, controlled trial with DASH eating plan plus physical activity improves fruit and vegetable intake and diastolic blood pressure in children.

Pacific Kids DASH for Health (PacDASH) aimed to improve child diet and physical activity (PA) level and prevent excess weight gain and elevation in blood pressure (BP) at 9 months.



PacDASH was a two-arm, randomized, controlled trial (ClinicalTrials.gov: NCT00905411). Eighty-five 5- to 8-year-olds in the 50th-99th percentile for BMI were randomly assigned to treatment (n=41) or control (n=44) groups; 62 completed the 9-month trial. Sixty-two percent were female. Mean age was 7.1±0.95 years. Race/ethnicity was Asian (44%), Native Hawaiian or Other Pacific Islander (28%), white (21%), or other race/ethnicity (7%). Intervention was provided at baseline and 3, 6 and 9 months, with monthly supportive mailings between intervention visits, and a follow-up visit at 15 months to observe maintenance. Diet and PA were assessed by 2-day log. Body size, composition, and BP were measured. The intervention effect on diet and PA, body size and composition, and BP by the end of the intervention was tested using an F test from a mixed regression model, after adjustment for sex, age, and ethnic group.



Fruit and vegetable (FV) intake decreased less in the treatment than control group (p=0.04). Diastolic BP (DBP) was 12 percentile units lower in the treatment than control group after 9 months of intervention (p=0.01). There were no group differences in systolic BP (SBP) or body size/composition.



The PacDASH trial enhanced FV intake and DBP, but not SBP or body size/composition.
Rule-based ontological knowledge base for monitoring partners across supply networks

This study demonstrates how ontology combined with semantic rules can be used for searching complete business partners in a supply network. Rather than being fully self-sufficient, individual enterprises are often part of a supply chain. Involving partners and understanding the importance of their activities is essential for enterprise agility. The question is how far-reaching and in what capacity a supply network is needed. Partner tracing becomes more difficult if search tasks involve potential partners or conforms to future production planning. This study utilized Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL) technologies to develop supply network ontology and a problem-solving ontology, respectively. The three objectives of the study are creating a conceptual knowledge model for describing supply partners and their relationships, developing semantic rules as problem-solving methods for partner tracing in response to research questions and gathering experimental facts for evaluating knowledgeintensive designs. An example in the solar power industry is used to explain the ontological knowledge design and its uses. The experiments in this study showed that ontological approaches can effectively manage intricate and dynamic partnerships among supply networks. This design is also scalable in both domain ontology and task ontology for solving more complex problems.
The differences in motivations of online game players and offline game players: A combined analysis of three studies at higher education level

Computer games have become a highly popular form of entertainment and have had a large impact on how University students spend their leisure time. Due to their highly motivating properties computer games have come to the attention of educationalists who wish to exploit these highly desirable properties for educational purposes. Several studies have been performed looking at motivations for playing computer games in a general context and in a Higher Education (HE) context. These studies did not focus on the differences in motivations between online and offline game players. Equally the studies did not look at the differences in motivations of people who prefer single player games and people who prefer multiplayer games. If games-based learning is to become a recognised teaching approach then such motivations for playing computer games must be better understood. This paper presents the combined analysis of three studies at HE level, performed over a four year period from 2005 to 2009. The paper focuses on differences of motivations in relation to single player/multiplayer preference and online/ offline game participation. The study found that challenge is the top ranking motivation and recognition is the lowest ranking motivation for playing computer games in general. Challenge is also the top ranking motivation for playing games in HE while fantasy and recognition are the lowest ranking motivations for playing games in HE. Multiplayer gamers derive more competition, cooperation, recognition, fantasy and curiosity from playing games and online gamers derive more challenge, cooperation, recognition and control from playing games. Multiplayer gamers and online gamers ranked competition, cooperation and recognition significantly more important for playing games in HE than single players and offline participants.
A model of noise reduction using Gabor Kuwahara filter

Noise analysis of the image is the most important part in image processing. A filter is designed to improve the Kuwahara filter to reducing the noise. The Proposed Gabor Kuwahara filter is efficient to reduce the noise without losing the information on edges, before the preparation of the images for segmentation and other image processing techniques. This proposed noise reduction model is automated algorithm incorporated with Gabor transformation. The proposed model results the filtered image without blurring the edges and provides fine quality of images by reducing noise. The experimental results of MSE (Mean Square Error) and SSIM (Structural Similarity Index Measure) using the proposed model is compared with other state-of-art noise reduction method and provides many promising results.
As time goes by: hippocampal connectivity changes with remoteness of autobiographical memory retrieval.

The hippocampus is crucial for episodic autobiographical memory retrieval. Functional neuroimaging evidence suggests that it is similarly engaged in recent and remote retrieval when memories are matched on vividness and personal importance. Far fewer studies have investigated the nature of hippocampal-neocortical coactivation in relation to memory remoteness. The purpose of this study was to examine hippocampal activity and functional connectivity as a function of memory age. Unlike most studies of autobiographical memory, we included autobiographical memories formed in the days and weeks before scanning, in addition to truly remote memories on the order of months and years. Like previous studies, we found that the hippocampus was active bilaterally regardless of memory age, with anterior activity increasing up to 1 yr and then decreasing, and with posterior activity being less sensitive to memory age. More importantly, hippocampal functional connectivity varied with memory age. Retrieving recent memories (≤1 yr) showed a late coactivation of the hippocampus and areas of the autobiographical memory network, whereas retrieving remote memories (10 yrs) showed an early negative coactivation of the hippocampus and left inferior frontal gyrus followed by a positive coactivation with anterior cingulate. This finding may reflect that the hippocampus is more strongly integrated with the autobiographical memory network for recent than for remote memories, and that more effort is required to recover remote memories.
When Good Becomes Evil: Keystroke Inference with Smartwatch

One rising trend in today's consumer electronics is the wearable devices, e.g., smartwatches. With tens of millions of smartwatches shipped, however, the security implications of such devices are not fully understood. Although previous studies have pointed out some privacy concerns about the data that can be collected, like personalized health information, the threat is considered low as the leaked data is not highly sensitive and there is no real attack implemented. In this paper we investigate a security problem coming from sensors in smartwatches, especially the accelerometer. The results show that the actual threat is much beyond people's awareness. Being worn on the wrist, the accelerometer built within a smartwatch can track user's hand movements, which makes inferring user inputs on keyboards possible in theory. But several challenges need to be addressed ahead in the real-world settings: e.g., small and irregular hand movements occur persistently during typing, which degrades the tracking accuracy and sometimes even overwhelms useful signals.
 In this paper, we present a new and practical side-channel attack to infer user inputs on keyboards by exploiting sensors in smartwatch. Novel keystroke inference models are developed to mitigate the negative impacts of tracking noises. We focus on two major categories of keyboards: one is numeric keypad that is generally used to input digits, and the other is QWERTY keyboard on which a user can type English text. Two prototypes have been built to infer users' banking PINs and English text when they type on POS terminal and QWERTY keyboard respectively. Our results show that for numeric keyboard, the probability of finding banking PINs in the top 3 candidates can reach 65%, while for QWERTY keyboard, a significant accuracy improvement is achieved compared to the previous works, especially of the success rate of finding the correct word in the top 10 candidates.
Characterizing Email Search using Large-scale Behavioral Logs and Surveys

As the number of email users and messages continues to grow, search is becoming more important for finding information in personal archives. In spite of its importance, email search is much less studied than web search, particularly using large-scale behavioral log analysis. In this paper we report the results of a large-scale log analysis of email search and complement this with a survey to better understand email search intent and success. We characterize email search behaviors and highlight differences from web search. When searching for email, people know many attributes about what they are looking for; they often look for specific known items; their queries are shorter and they click on fewer items than in web search. Although repeat queries are common in both email and web search, repeat visits to the same search result are much less common in email search suggesting that the same query is used for different search intents over time. We consider search intent from multiple angles. In email search logs, we find that people use email search not just to find information but also to perform tasks such as cleanup or organization, and that the distribution of actions they perform depends on the type of query. In our survey, people reported that they looked for specific information in both email search and web search, but they were much less likely to search for general information on a topic in email. The differences in overall behavior, re-finding patterns and search intents we observed between email and web search have important implications for the design of email search algorithms and interfaces.
A survey on provenance: What for? What form? What from?

Provenance refers to any information describing the production process of an end product, which can be anything from a piece of digital data to a physical object. While this survey focuses on the former type of end product, this definition still leaves room for many different interpretations of and approaches to provenance. These are typically motivated by different application domains for provenance (e.g., accountability, reproducibility, process debugging) and varying technical requirements such as runtime, scalability, or privacy. As a result, we observe a wide variety of provenance types and provenance-generating methods. This survey provides an overview of the research field of provenance, focusing on what provenance is used for (what for?), what types of provenance have been defined and captured for the different applications (what form?), and which resources and system requirements impact the choice of deploying a particular provenance solution (what from?). For each of these three key questions, we provide a classification and review the state of the art for each class. We conclude with a summary and possible future research challenges.
DC-NMF: nonnegative matrix factorization based on divide-and-conquer for fast clustering and topic modeling

The importance of unsupervised clustering and topic modeling is well recognized with ever-increasing volumes of text data available from numerous sources. Nonnegative matrix factorization (NMF) has proven to be a successful method for cluster and topic discovery in unlabeled data sets. In this paper, we propose a fast algorithm for computing NMF using a divide-and-conquer strategy, called DC-NMF. Given an input matrix where the columns represent data items, we build a binary tree structure of the data items using a recently-proposed efficient algorithm for computing rank-2 NMF, and then gather information from the tree to initialize the rank-k NMF, which needs only a few iterations to reach a desired solution. We also investigate various criteria for selecting the node to split when growing the tree. We demonstrate the scalability of our algorithm for computing general rank-k NMF as well as its effectiveness in clustering and topic modeling for large-scale text data sets, by comparing it to other frequently utilized state-of-the-art algorithms. The value of the proposed approach lies in the highly efficient and accurate method for initializing rank-k NMF and the scalability achieved from the divide-and-conquer approach of the algorithm and properties of rank-2 NMF. In summary, we present efficient tools for analyzing large-scale data sets, and techniques that can be generalized to many other data analytics problem domains along with an open-source software library called SmallK.
An Exploration of Eye Gaze in Spoken Language Processing for Multimodal Conversational Interfaces

Motivated by psycholinguistic findings, we are currently investigating the role of eye gaze in spoken language understanding for multimodal conversational systems. Our assumption is that, during human machine conversation, a user’s eye gaze on the graphical display indicates salient entities on which the user’s attention is focused. The specific domain information about the salient entities is likely to be the content of communication and therefore can be used to constrain speech hypotheses and help language understanding. Based on this assumption, this paper describes an exploratory study that incorporates eye gaze in salience modeling for spoken language processing. Our empirical results show that eye gaze has a potential in improving automated language processing. Eye gaze is subconscious and involuntary during human machine conversation. Our work motivates more in-depth investigation on eye gaze in attention prediction and its implication in automated language processing.
On (Commercial) Benefits of Automatic Text Summarization Systems in the News Domain: A Case of Media Monitoring and Media Response Analysis

In this work, we present the results of a systematic study to investigate the (commercial) benefits of automatic text summarization systems in a real world scenario. More specifically, we define a use case in the context of media monitoring and media response analysis and claim that even using a simple query-based extractive approach can dramatically save the processing time of the employees without significantly reducing the quality of their work.
Semantic Information Modeling for Emerging Applications in Smart Grid

Smart Grid modernizes power grid by integrating digital and information technologies. Millions of smart meters, intelligent appliances and communication infrastructures are under deployment allowing advanced IT applications to be developed to secure and manage power grid operations. Demand response (DR) is one such emerging application to optimize electricity demand by curtailing/shifting power load when peak load occurs. Existing DR approaches are mostly based on static plans such as pricing policies and load shedding schedules. However, improvements to power management applications rely on data emanating from existing and new information sources with the growth of Smart Grid information space. In particular, dynamic DR algorithms depend on information from smart meters that report interval-based power consumption measurement, HVAC systems that monitor buildings heat and humidity, and even weather forecast services. In order for emerging Smart Grid applications to take advantage of the diverse data influx, extensible information integration is required. In this paper, we develop an integrated Smart Grid information model using Semantic Web techniques and present case studies of using semantic information for dynamic DR. We show the semantic model facilitates information integration and knowledge representation for developing the next generation Smart Grid applications.
Automated Whitebox Fuzz Testing

Fuzz testing is an effective technique for finding security vulnerabilities in software. Traditionally, fuzz testing tools apply random mutations to well-formed inputs of a program and test the resulting values. We present an alternative whitebox fuzz testing approach inspired by recent advances in symbolic execution and dynamic test generation. Our approach records an actual run of the program under test on a well-formed input, symbolically evaluates the recorded trace, and gathers constraints on inputs capturing how the program uses these. The collected constraints are then negated one by one and solved with a constraint solver, producing new inputs that exercise different control paths in the program. This process is repeated with the help of a code-coverage maximizing heuristic designed to find defects as fast as possible. We have implemented this algorithm in SAGE (Scalable, Automated, Guided Execution), a new tool employing x86 instruction-level tracing and emulation for whitebox fuzzing of arbitrary file-reading Windows applications. We describe key optimizations needed to make dynamic test generation scale to large input files and long execution traces with hundreds of millions of instructions. We then present detailed experiments with several Windows applications. Notably, without any format-specific knowledge, SAGE detects the MS07-017 ANI vulnerability, which was missed by extensive blackbox fuzzing and static analysis tools. Furthermore, while still in an early stage of development, SAGE has already discovered 30+ new bugs in large shipped Windows applications including image processors, media players, and file decoders. Several of these bugs are potentially exploitable memory access violations.
Iteratively Re-weighted Least Squares Minimization for Sparse Recovery

Under certain conditions (known as the Restricted Isometry Property or RIP) on the m×N matrix Φ (where m < N), vectors x ∈ R that are sparse (i.e. have most of their entries equal to zero) can be recovered exactly from y := Φx even though Φ(y) is typically an (N − m)dimensional hyperplane; in addition x is then equal to the element in Φ(y) of minimal l1-norm. This minimal element can be identified via linear programming algorithms. We study an alternative method of determining x, as the limit of an Iteratively Re-weighted Least Squares (IRLS) algorithm. The main step of this IRLS finds, for a given weight vector w, the element in Φ(y) with smallest l2(w)-norm. If x (n) is the solution at iteration step n, then the new weight w is defined by w (n) i := [ |x i |2 + ǫn ] −1/2 , i = 1, . . . , N , for a decreasing sequence of adaptively defined ǫn; this updated weight is then used to obtain x (n+1) and the process is repeated. We prove that when Φ satisfies the RIP conditions, the sequence x converges for all y, regardless of whether Φ(y) contains a sparse vector. If there is a sparse vector in Φ(y), then the limit is this sparse vector, and when x is sufficiently close to the limit, the remaining steps of the algorithm converge exponentially fast (linear convergence in the terminology of numerical optimization). The same algorithm with the “heavier” weight w (n) i = [ |x i |2 + ǫn ] −1+τ/2 , i = 1, . . . , N , where 0 < τ < 1, can recover sparse solutions as well; more importantly, we show its local convergence is superlinear and approaches a quadratic rate for τ approaching to zero.
Pocket reflectometry

We present a simple, fast solution for reflectance acquisition using tools that fit into a pocket. Our method captures video of a flat target surface from a fixed video camera lit by a hand-held, moving, linear light source. After processing, we obtain an SVBRDF.
 We introduce a BRDF chart, analogous to a color "checker" chart, which arranges a set of known-BRDF reference tiles over a small card. A sequence of light responses from the chart tiles as well as from points on the target is captured and matched to reconstruct the target's appearance.
 We develop a new algorithm for BRDF reconstruction which works directly on these LDR responses, without knowing the light or camera position, or acquiring HDR lighting. It compensates for spatial variation caused by the local (finite distance) camera and light position by warping responses over time to align them to a specular reference. After alignment, we find an optimal linear combination of the Lambertian and purely specular reference responses to match each target point's response. The same weights are then applied to the corresponding (known) reference BRDFs to reconstruct the target point's BRDF. We extend the basic algorithm to also recover varying surface normals by adding two spherical caps for diffuse and specular references to the BRDF chart.
 We demonstrate convincing results obtained after less than 30 seconds of data capture, using commercial mobile phone cameras in a casual environment.