nils-herrmann/clean_scirepeval huggingface dataset:
Understanding optimal caching and opportunistic caching at "the edge" of information-centric networks

A formal framework is presented for the characterization of cache allocation models in Information-Centric Networks (ICN). The framework is used to compare the performance of optimal caching everywhere in an ICN with opportunistic caching of content only near its consumers. This comparison is made using the independent reference model adopted in all prior studies, as well as a new model that captures non-stationary reference locality in space and time. The results obtained analytically and from simulations show that optimal caching throughout an ICN and opportunistic caching at the edge routers of an ICN perform comparably the same. In addition caching content opportunistically only near its consumers is shown to outperform the traditional on-path caching approach assumed in most ICN architectures in an unstructured network with arbitrary topology represented as a random geometric graph.
A bilingual advantage in task switching

This study investigated the possibility that lifelong bilingualism may lead to enhanced efficiency in the ability to shift between mental sets. We compared the performance of monolingual and fluent bilingual college students in a task-switching paradigm. Bilinguals incurred reduced switching costs in the task-switching paradigm when compared with monolinguals, suggesting that lifelong experience in switching between languages may contribute to increased efficiency in the ability to shift flexibly between mental sets. On the other hand, bilinguals did not differ from monolinguals in the differential cost of performing mixed-task as opposed to single-task blocks. Together, these results indicate that bilingual advantages in executive function most likely extend beyond inhibition of competing responses, and encompass flexible mental shifting as well.
Boosting and Rocchio Applied to Text Filtering

We discuss two learning algorithms for text filtering: modified Rocchio and a boosting algorithm called AdaBoost. We show how both algorithms can be adapted to maximize any general utility matrix that associates cost (or gain) for each pair of machine prediction and correct label. We first show that AdaBoost significantly outperforms another highly effective text filtering algorithm. We then compare AdaBoost and Rocchio over three large text filtering tasks. Overall both algorithms are comparable and are quite effective. AdaBoost produces better classifiers than Rocchio when the training collection contains a very large number of relevant documents. However, on these tasks, Rocchio runs much faster than AdaBoost.
On Validity of Program Transformations in the Java Memory Model

We analyse the validity of several common program transformations in multi-threaded Java, as defined by the Java Memory Model (JMM) section of Chapter 17 of the Java Language Specification. The main design goal of the JMM was to allow as many optimisations as possible. However, we find that commonly used optimisations, such as common subexpression elimination, can introduce new behaviours and so are invalid for Java. In this paper, we describe several kinds of transformations and explain the problems with a number of counterexamples. More positively, we also examine some valid transformations, and prove their validity. Our study contributes to the understanding of the JMM, and has the practical impact of revealing some cases where the Sun Hotspot JVM does not comply with the Java Memory Model.
Deep Learning Strong Parts for Pedestrian Detection

Recent advances in pedestrian detection are attained by transferring the learned features of Convolutional Neural Network (ConvNet) to pedestrians. This ConvNet is typically pre-trained with massive general object categories (e.g. ImageNet). Although these features are able to handle variations such as poses, viewpoints, and lightings, they may fail when pedestrian images with complex occlusions are present. Occlusion handling is one of the most important problem in pedestrian detection. Unlike previous deep models that directly learned a single detector for pedestrian detection, we propose DeepParts, which consists of extensive part detectors. DeepParts has several appealing properties. First, DeepParts can be trained on weakly labeled data, i.e. only pedestrian bounding boxes without part annotations are provided. Second, DeepParts is able to handle low IoU positive proposals that shift away from ground truth. Third, each part detector in DeepParts is a strong detector that can detect pedestrian by observing only a part of a proposal. Extensive experiments in Caltech dataset demonstrate the effectiveness of DeepParts, which yields a new state-of-the-art miss rate of 11:89%, outperforming the second best method by 10%.
A quantitative comparison of graph-based models for Internet topology

Graphs are commonly used to model the topological structure of internetworks, to study problems ranging from routing to resource reservation. A variety of graphs are found in the literature, including xed topologies such as rings or stars, \well-known" topologies such as the ARPAnet, and randomly generated topologies. While many researchers rely upon graphs for analytic and simulation studies, there has been little analysis of the implications of using a particular model, or how the graph generation method may a ect the results of such studies. Further, the selection of one generation method over another is often arbitrary, since the di erences and similarities between methods are not well
FFT-Based BP Decoding of General LDPC Codes Over Abelian Groups

We introduce a wide class of low-density parity-check (LDPC) codes, large enough to include LDPC codes over finite fields, rings, or groups, as well as some nonlinear codes. A belief-propagation decoding procedure with the same complexity as for the decoding of LDPC codes over finite fields is also presented. Moreover, an encoding procedure is developed
SIMPATH: An Efficient Algorithm for Influence Maximization under the Linear Threshold Model

There is significant current interest in the problem of influence maximization: given a directed social network with influence weights on edges and a number k, find k seed nodes such that activating them leads to the maximum expected number of activated nodes, according to a propagation model. Kempe et al. showed, among other things, that under the Linear Threshold Model, the problem is NP-hard, and that a simple greedy algorithm guarantees the best possible approximation factor in PTIME. However, this algorithm suffers from various major performance drawbacks. In this paper, we propose Simpath, an efficient and effective algorithm for influence maximization under the linear threshold model that addresses these drawbacks by incorporating several clever optimizations. Through a comprehensive performance study on four real data sets, we show that Simpath consistently outperforms the state of the art w.r.t. running time, memory consumption and the quality of the seed set chosen, measured in terms of expected influence spread achieved.
Experiences using systematic review guidelines

A systematic review is a defined and methodical way to identify, assess and analyse published primary studies in order to investigate a specific research question. Kitchenham has recently published guidelines for software engineering researchers performing systematic reviews. The objective of our paper is to critique Kitchenham’s guidelines and to comment on systematic review generally with respect to our experiences conducting our first systematic review. Our perspective as neophytes may be particularly illuminating for other software engineering researchers who are also considering conducting their first systematic review. Overall we can recommend Kitchenham’s guidelines to other researchers considering systematic reviews. We caution researchers to clearly and narrowly define the research questions they will investigate by systematic review, to reduce the overall effort and to improve the quality of the selection of papers and extraction of data. In particular we recommend defining complementary research questions that are not within the scope of the systematic review in order to clarify the boundaries of the specific research question of interest. An instance of this recommendation is that researchers should clearly define the unit of study for the systematic review.
Latent dirichlet allocation for tag recommendation

Tagging systems have become major infrastructures on the Web. They allow users to create tags that annotate and categorize content and share them with other users, very helpful in particular for searching multimedia content. However, as tagging is not constrained by a controlled vocabulary and annotation guidelines, tags tend to be noisy and sparse. Especially new resources annotated by only a few users have often rather idiosyncratic tags that do not reflect a common perspective useful for search. In this paper we introduce an approach based on Latent Dirichlet Allocation (LDA) for recommending tags of resources in order to improve search. Resources annotated by many users and thus equipped with a fairly stable and complete tag set are used to elicit latent topics to which new resources with only a few tags are mapped. Based on this, other tags belonging to a topic can be recommended for the new resource. Our evaluation shows that the approach achieves significantly better precision and recall than the use of association rules, suggested in previous work, and also recommends more specific tags. Moreover, extending resources with these recommended tags significantly improves search for new resources.
Identifying Word Relations in Software: A Comparative Study of Semantic Similarity Tools

Modern software systems are typically large and complex, making comprehension of these systems extremely difficult. Experienced programmers comprehend code by seamlessly processing synonyms and other word relations. Thus, we believe that automated comprehension and software tools can be significantly improved by leveraging word relations in software. In this paper, we perform a comparative study of six state of the art, English-based semantic similarity techniques and evaluate their effectiveness on words from the comments and identifiers in software. Our results suggest that applying English-based semantic similarity techniques to software without any customization could be detrimental to the performance of the client software tools. We propose strategies to customize the existing semantic similarity techniques to software, and describe how various program comprehension tools can benefit from word relation information.
A Systematic Framework for Designing and Evaluating Persuasive Systems

Affective Loop Experiences What Are They? p. 1 Fine Processing p. 13 Mass Interpersonal Persuasion: An Early View of a New Phenomenon p. 23 Social Network Systems Online Persuasion in Facebook and Mixi: A Cross-Cultural Comparison p. 35 Website Credibility, Active Trust and Behavioural Intent p. 47 Network Awareness, Social Context and Persuasion p. 58 Knowledge Management Persuasion in Knowledge-Based Recommendation p. 71 Persuasive Technology Design A Rhetorical Approach p. 83 Benevolence and Effectiveness: Persuasive Technology's Spillover Effects in Retail Settings p. 94
A Virtual Machine Placement Taxonomy

Cloud computing data enters dynamically provide millions of virtual machines (VMs) in actual cloud markets. In this context, Virtual Machine Placement (VMP) is one of the most challenging problems in cloud infrastructure management, considering the large number of possible optimization criteria and different formulations that could be studied. VMP literature include relevant research topics such as energy efficiency, Service Level Agreement (SLA), Quality of Service (QoS), cloud service pricing schemes and carbon dioxide emissions, all of them with high economical and ecological impact. This work classifies an extensive up-to-date survey of the most relevant VMP literature proposing a novel taxonomy in order to identify research opportunities and define a general vision on this research area.
Quantizing deep convolutional networks for efficient inference: A whitepaper

We present an overview of techniques for quantizing convolutional neural networks for inference with integer weights and activations. 1. Per-channel quantization of weights and per-layer quantization of activations to 8-bits of precision post-training produces classification accuracies within 2% of floating point networks for a wide variety of CNN architectures (section 3.1). 2. Model sizes can be reduced by a factor of 4 by quantizing weights to 8bits, even when 8-bit arithmetic is not supported. This can be achieved with simple, post training quantization of weights (section 3.1). 3. We benchmark latencies of quantized networks on CPUs and DSPs and observe a speedup of 2x-3x for quantized implementations compared to floating point on CPUs. Speedups of up to 10x are observed on specialized processors with fixed point SIMD capabilities, like the Qualcomm QDSPs with HVX (section 6). 4. Quantization-aware training can provide further improvements, reducing the gap to floating point to 1% at 8-bit precision. Quantization-aware training also allows for reducing the precision of weights to four bits with accuracy losses ranging from 2% to 10%, with higher accuracy drop for smaller networks (section 3.2). 5. We introduce tools in TensorFlow and TensorFlowLite for quantizing convolutional networks (Section 3). 6. We review best practices for quantization-aware training to obtain high accuracy with quantized weights and activations (section 4). 7. We recommend that per-channel quantization of weights and per-layer quantization of activations be the preferred quantization scheme for hardware acceleration and kernel optimization. We also propose that future processors and hardware accelerators for optimized inference support precisions of 4, 8 and 16 bits (section 7).
Multi-Document Summarization via the Minimum Dominating Set

Multi-document summarization has been an important problem in information retrieval. It aims to distill the most important information from a set of documents to generate a compressed summary. Given a sentence graph generated from a set of documents where vertices represent sentences and edges indicate that the corresponding vertices are similar, the extracted summary can be described using the idea of graph domination. In this paper, we propose a new principled and versatile framework for multi-document summarization using the minimum dominating set. We show that four well-known summarization tasks including generic, query-focused, update, and comparative summarization can be modeled as different variations derived from the proposed framework. Approximation algorithms for performing summarization are also proposed and empirical experiments are conducted to demonstrate the effectiveness of our proposed framework.
SmartCrawler: A Two-Stage Crawler for Efficiently Harvesting Deep-Web Interfaces

As deep web grows at a very fast pace, there has been increased interest in techniques that help efficiently locate deep-web interfaces. However, due to the large volume of web resources and the dynamic nature of deep web, achieving wide coverage and high efficiency is a challenging issue. We propose a two-stage framework, namely SmartCrawler, for efficient harvesting deep web interfaces. In the first stage, SmartCrawler performs site-based searching for center pages with the help of search engines, avoiding visiting a large number of pages. To achieve more accurate results for a focused crawl, SmartCrawler ranks websites to prioritize highly relevant ones for a given topic. In the second stage, SmartCrawler achieves fast in-site searching by excavating most relevant links with an adaptive link-ranking. To eliminate bias on visiting some highly relevant links in hidden web directories, we design a link tree data structure to achieve wider coverage for a website. Our experimental results on a set of representative domains show the agility and accuracy of our proposed crawler framework, which efficiently retrieves deep-web interfaces from large-scale sites and achieves higher harvest rates than other crawlers.
MegaMIMO : Scaling Wireless Capacity with User Demands

We present MegaMIMO, a joint multi-user beamforming system that enables independent access points (APs) to beamform their signals, and communicate with their clients on the same channel as if they were one large MIMO transmitter. The key enabling technology behind MegaMIMO is a new low-overhead technique for synchronizing the phase of multiple transmitters in a distributed manner. The design allows a wireless LAN to scale its throughput by continually adding more APs on the same channel. MegaMIMO is implemented and tested with both software radio clients and off-the-shelf 802.11n cards, and evaluated in a dense congested deployment resembling a conference room. Results from a 10-AP software-radio testbed show a linear increase in network throughput with a median gain of 8.1 to 9.4×. Our results also demonstrate that MegaMIMO’s joint multi-user beamforming can provide throughput gains with unmodified 802.11n cards.
Data compression via textual substitution

A general model for data compression which includes most data compression systems in the fiterature as special cases is presented. Macro schemes are based on the principle of finding redundant strings or patterns and replacing them by pointers to a common copy. Different varieties of macro schemes may be defmed by specifying the meaning of a pointer; that is, a pointer may indicate a substring of the compressed string, a substring of the original string, or a substring of some other string such as an external dictionary. Other varieties of macro schemes may be defined by restricting the type of overlapping or recursion that may be used. Trade-offs between different varieties of macro schemes, exact lower bounds on the amount of compression obtainable, and the complexity of encoding and decoding are discussed, as well as how the work of other authors relates to this model.
Mining concepts from code with probabilistic topic models

We develop and apply statistical topic models to software as a means of extracting concepts from source code. The effectiveness of the technique is demonstrated on 1,555 projects from SourceForge and Apache consisting of 113,000 files and 19 million lines of code. In addition to providing an automated, unsupervised, solution to the problem of summarizing program functionality, the approach provides a probabilistic framework with which to analyze and visualize source file similarity. Finally, we introduce an information-theoretic approach for computing tangling and scattering of extracted concepts, and present preliminary results
ARIMA Models to Predict Next-Day Electricity Prices

Price forecasting is becoming increasingly relevant to producers and consumers in the new competitive electric power markets. Both for spot markets and long-term contracts, price forecasts are necessary to develop bidding strategies or negotiation skills in order to maximize benefit. This paper provides a method to predict next-day electricity prices based on the ARIMA methodology. ARIMA techniques are used to analyze time series and, in the past, have been mainly used for load forecasting due to their accuracy and mathematical soundness. A detailed explanation of the aforementioned ARIMA models and results from mainland Spain and Californian markets are presented.
Mindfulness-based stress reduction for older adults: effects on executive function, frontal alpha asymmetry and immune function.

Mindfulness-based stress reduction (MBSR) has enhanced cognition, positive emotion, and immunity in younger and middle-aged samples; its benefits are less well known for older persons. Here we report on a randomized controlled trial of MBSR for older adults and its effects on executive function, left frontal asymmetry of the EEG alpha band, and antibody response.



Older adults (n = 201) were randomized to MBSR or waiting list control. The outcome measures were: the Trail Making Test part B/A (Trails B/A) ratio, a measure of executive function; changes in left frontal alpha asymmetry, an indicator of positive emotions or approach motivation; depression, mindfulness, and perceived stress scores, and the immunoglobulin G response to a protein antigen, a measure of adaptive immunity.



MBSR participants had a lower Trails B/A ratio immediately after intervention (p < 0.05); reduced shift to rightward frontal alpha activation after intervention (p = 0.03); higher baseline antibody levels after intervention (p < 0.01), but lower antibody responses 24 weeks after antigen challenge (p < 0.04), and improved mindfulness after intervention (p = 0.023) and at 21 weeks of follow-up (p = 0.006).



MBSR produced small but significant changes in executive function, mindfulness, and sustained left frontal alpha asymmetry. The antibody findings at follow-up were unexpected. Further study of the effects of MBSR on immune function should assess changes in antibody responses in comparison to T-cell-mediated effector functions, which decline as a function of age.
Deep learning framework for detection of hypoglycemic episodes in children with type 1 diabetes

Most Type 1 diabetes mellitus (T1DM) patients have hypoglycemia problem. Low blood glucose, also known as hypoglycemia, can be a dangerous and can result in unconsciousness, seizures and even death. In recent studies, heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal are found as the most common physiological parameters to be effected from hypoglycemic reaction. In this paper, a state-of-the-art intelligent technology namely deep belief network (DBN) is developed as an intelligent diagnostics system to recognize the onset of hypoglycemia. The proposed DBN provides a superior classification performance with feature transformation on either processed or un-processed data. To illustrate the effectiveness of the proposed hypoglycemia detection system, 15 children with Type 1 diabetes were volunteered overnight. Comparing with several existing methodologies, the experimental results showed that the proposed DBN outperformed and achieved better classification performance.
Autonomous Rover Navigation on Unknown Terrains Functions and Integration

Autonomous long range navigation in partially known planetary-like terrain is an open challenge for robotics. Navigating several hundreds of meters without any human intervention requires the robot to be able to build various representations of its environment, to plan and execute trajectories according to the kind of terrain traversed, to localize itself as it moves, and to schedule, start, control and interrupt these various activities. In this paper, we brie y describe some functionalities that are currently running on board the Marsokhod model robot Lama at LAAS/CNRS. We then focus on the necessity to integrate various instances of the perception and decision functionalities, and on the di culties raised by this integration.