nils-herrmann/clean_scirepeval huggingface dataset:
The Echo Chamber Effect in Twitter: does community polarization increase?

A recent article criticized social media platforms for failing to “mobilize society into action” long enough to address any major global issue. This is attributed to the simplistic design of current social media platforms, which encourage ideas to spread virally but do not support consensus formation which might lead to lasting social change. One reason for this could be the well known “echo chamber” phenomenon, whereby people tend to discuss issues only with other like-minded people. Social media has been blamed for encouraging the echo chamber effect and increasing polarization in society. For example, in Twitter, it is very common for users to be “followed” by others with similar views. Is this a reflection of real life or does Twitter actually increase polarization of views? This paper investigates this by comparing the Twitter follows network at two points in time and detecting communities in the network of reciprocated follows relationships. We find that new edges are (at least 3-4 times) more likely to be created inside existing communities than between communities, and existing edges are more likely to be removed if they are between communities. This leads to the conclusion that Twitter communities are indeed becoming more polarized as time passes.
A Semantic Space for Music Derived from Social Tags

In this paper we investigate social tags as a novel highvolume source of semantic metadata for music, using techniques from the fields of information retrieval and multivariate data analysis. We show that, despite the ad hoc and informal language of tagging, tags define a low-dimensional semantic space that is extremely well-behaved at the track level, in particular being highly organised by artist and musical genre. We introduce the use of Correspondence Analysis to visualise this semantic space, and show how it can be applied to create a browse-by-mood interface for a psychologically-motivated two-dimensional subspace rep resenting musical emotion.
Nooks: an architecture for reliable device drivers

With the enormous growth in processor performance over the last decade, it is clear that reliability, rather than performance, is now the greatest challenge for computer systems research. This is particularly true in the context of Internet services that require 24x7 operation and home computers with no professional administration. While operating system products have matured and become more reliable, they are still the source of a significant number of failures. Furthermore, recent studies show that device drivers are frequently responsible for operating system failures. For example, a study at Stanford University found that Linux drivers have 3 to 7 times the bug frequency as the rest of the OS . An analysis of product support calls for Windows 2000 showed that device drivers accounted for 27% of crashes, compared to 2% for the kernel itself [16].
A Lesson on Authentication Protocol Design

The purpose of this note is to describe a useful lesson we learned on authentication protocol design. In a recent article , we presented a simple authentication protocol to illustrate the concept of a trusted server. The protocol has a flaw, which was brought to our attention by Mart~n Abadi of DEC. In what follows, we first describe the protocol and its flaw, and how the flaw-was introduced in the process of deriving the protocol from its correct full information version. We then introduce a principle, called the Principle of Full Information, and explain how its use could have prevented the protocol flaw. We believe the Principle of Full Information is a useful authentication protocol design principle, and advocate its use. Lastly, we present several heuristics for simplifying full information protocols and illustrate their application to a mutual authentication protocol.
Automating Network Application Dependency Discovery: Experiences, Limitations, and New Solutions

Large enterprise networks consist of thousands of services and applications. The performance and reliability of any particular application may depend on multiple services, spanning many hosts and network components. While the knowledge of such dependencies is invaluable for ensuring the stability and efficiency of these applications, thus far the only proven way to discover these complex dependencies is by exploiting human expert knowledge, which does not scale with the number of applications in large enterprises. Recently, researchers have proposed automated discovery of dependencies from network traffic [8, 18]. In this paper, we present a comprehensive study of the performance and limitations of this class of dependency discovery techniques (including our own prior work), by comparing with the ground truth of five dominant Microsoft applications. We introduce a new system, Orion, that discovers dependencies using packet headers and timing information in network traffic based on a novel insight of delay spike based analysis. Orion improves the state of the art significantly, but some shortcomings still remain. To take the next step forward, Orion incorporates external tests to reduce errors to a manageable level. Our results showOrion provides a solid foundation for combining automated discovery with simple testing to obtain accurate and validated dependencies.
Rain or Snow Detection in Image Sequences Through Use of a Histogram of Orientation of Streaks

The detection of bad weather conditions is crucial for meteorological centers, specially with demand for air, sea and ground traffic management. In this article, a system based on computer vision is presented which detects the presence of rain or snow. To separate the foreground from the background in image sequences, a classical Gaussian Mixture Model is used. The foreground model serves to detect rain and snow, since these are dynamic weather phenomena. Selection rules based on photometry and size are proposed in order to select the potential rain streaks. Then a Histogram of Orientations of rain or snow Streaks (HOS), estimated with the method of geometric moments, is computed, which is assumed to follow a model of Gaussian-uniform mixture. The Gaussian distribution represents the orientation of the rain or the snow whereas the uniform distribution represents the orientation of the noise. An algorithm of expectation maximization is used to separate these two distributions. Following a goodness-of-fit test, the Gaussian distribution is temporally smoothed and its amplitude allows deciding the presence of rain or snow. When the presence of rain or of snow is detected, the HOS makes it possible to detect the pixels of rain or of snow in the foreground images, and to estimate the intensity of the precipitation of rain or of snow. The applications of the method are numerous and include the detection of critical weather conditions, the observation of weather, the reliability improvement of video-surveillance systems and rain rendering.
Addressee and Response Selection for Multi-Party Conversation

To create conversational systems working in actual situations, it is crucial to assume that they interact with multiple agents. In this work, we tackle addressee and response selection for multi-party conversation, in which systems are expected to select whom they address as well as what they say. The key challenge of this task is to jointly model who is talking about what in a previous context. For the joint modeling, we propose two modeling frameworks: 1) static modeling and 2) dynamic modeling. To show benchmark results of our frameworks, we created a multi-party conversation corpus. Our experiments on the dataset show that the recurrent neural network based models of our frameworks robustly predict addressees and responses in conversations with a large number of agents.
Examining Patterns of Influenza Vaccination in Social Media

Traditional data on influenza vaccination has several limitations: high cost, limited coverage of underrepresented groups, and low sensitivity to emerging public health issues. Social media, such as Twitter, provide an alternative way to understand a population’s vaccination-related opinions and behaviors. In this study, we build and employ several natural language classifiers to examine and analyze behavioral patterns regarding influenza vaccination in Twitter across three dimensions: temporality (by week and month), geography (by US region), and demography (by gender). Our best results are highly correlated official government data, with a correlation over 0.90, providing validation of our approach. We then suggest a number of directions for future work.
User interface development for interactive television: extending a commercial DTV platform to the virtual channel API

We explore the generation of interactive computer graphics at digital set-top boxes in place of the fixed graphics that were embedded to the television video before the broadcast. This direction raises new requirements for user interface development, since the graphics are merged with video at each set-top box dynamically, without the traditional quality control from the television producers. Besides the technical issues, interactive computer graphics for television should be evaluated by television viewers. We employ an animated character in an interactive music television application that was evaluated by consumers, and was developed using the Virtual Channel Control Library, a custom high-level API, that was built using Microsoft Windows and TV technologies.
Why simple hash functions work: exploiting the entropy in a data stream

Hashing is fundamental to many algorithms and data structures widely used in practice. For theoretical analysis of hashing, there have been two main approaches. First, one can assume that the hash function is truly random, mapping each data item independently and uniformly to the range. This idealized model is unrealistic because a truly random hash function requires an exponential number of bits to describe. Alternatively, one can provide rigorous bounds on performance when explicit families of hash functions are used, such as 2-universal or O(1)-wise independent families. For such families, performance guarantees are often noticeably weaker than for ideal hashing.
 In practice, however, it is commonly observed that simple hash functions, including 2-universal hash functions, perform as predicted by the idealized analysis for truly random hash functions. In this paper, we try to explain this phenomenon. We demonstrate that the strong performance of universal hash functions in practice can arise naturally from a combination of the randomness of the hash function and the data. Specifially, following the large body of literature on random sources and randomness extraction, we model the data as coming from a "block source," whereby each new data item has some "entropy" given the previous ones. As long as the (Renyi) entropy per data item is sufficiently large, it turns out that the performance when choosing a hash function from a 2-universal family is essentially the same as for a truly random hash function. We describe results for several sample applications, including linear probing, balanced allocations, and Bloom filters.
A food image recognition system with Multiple Kernel Learning

Since health care on foods is drawing people's attention recently, a system that can record everyday meals easily is being awaited. In this paper, we propose an automatic food image recognition system for recording people's eating habits. In the proposed system, we use the Multiple Kernel Learning (MKL) method to integrate several kinds of image features such as color, texture and SIFT adaptively. MKL enables to estimate optimal weights to combine image features for each category. In addition, we implemented a prototype system to recognize food images taken by cellular-phone cameras. In the experiment, we have achieved the 61.34% classification rate for 50 kinds of foods. To the best of our knowledge, this is the first report of a food image classification system which can be applied for practical use.
EM Side-Channel Attacks on Commercial Contactless Smartcards Using Low-Cost Equipment

We introduce low-cost hardware for performing non-invasive side-channel attacks on Radio Frequency Identi cation Devices (RFID) and develop techniques for facilitating a correlation power analysis (CPA) in the presence of the eld of an RFID reader. We practically verify the e ectiveness of the developed methods by analysing the security of commercial contactless smartcards employing strong cryptography, pinpointing weaknesses in the protocol and revealing a vulnerability towards side-channel attacks. Employing the developed hardware, we present the rst successful key-recovery attack on commercially available contactless smartcards based on the Data Encryption Standard (DES) or TripleDES (3DES) cipher that are widely used for security-sensitive applications, e.g., payment purposes.
Securing software defined networks: taxonomy, requirements, and open issues

The emergence of SDNs promises to dramatically simplify network management and enable innovation through network programmability. Despite all the hype surrounding SDNs, exploiting its full potential is demanding. Security is still the key concern and is an equally striking challenge that reduces the growth of SDNs. Moreover, the deployment of novel entities and the introduction of several architectural components of SDNs pose new security threats and vulnerabilities. Besides, the landscape of digital threats and cyber-attacks is evolving tremendously, considering SDNs as a potential target to have even more devastating effects than using simple networks. Security is not considered as part of the initial SDN design; therefore, it must be raised on the agenda. This article discusses the state-of-the-art security solutions proposed to secure SDNs. We classify the security solutions in the literature by presenting a thematic taxonomy based on SDN layers/interfaces, security measures, simulation environments, and security objectives. Moreover, the article points out the possible attacks and threat vectors targeting different layers/interfaces of SDNs. The potential requirements and their key enablers for securing SDNs are also identified and presented. Also, the article gives great guidance for secure and dependable SDNs. Finally, we discuss open issues and challenges of SDN security that may be deemed appropriate to be tackled by researchers and professionals in the future.
A breakthrough in Speech emotion recognition using Deep Retinal Convolution Neural Networks

2. School of Electronics Engineering and Computer science. Peking University, Beijing 100871,China Speech emotion recognition (SER) is to study the formation and change of speaker’s emotional state from the speech signal perspective, so as to make the interaction between human and computer more intelligent. SER is a challenging task that has encountered the problem of less training data and low prediction accuracy. Here we propose a data augmentation algorithm based on the imaging principle of the retina and convex lens, to acquire the different sizes of spectrogram and increase the amount of training data by changing the distance between the spectrogram and the convex lens. Meanwhile, with the help of deep learning to get the high-level features, we propose the Deep Retinal Convolution Neural Networks (DRCNNs) for SER and achieve the average accuracy over 99%. The experimental results indicate that DRCNNs outperforms the previous studies in terms of both the number of emotions and the accuracy of recognition. Predictably, our results will dramatically improve human-computer interaction.
Google trends: a web-based tool for real-time surveillance of disease outbreaks.

Google Flu Trends can detect regional outbreaks of influenza 7-10 days before conventional Centers for Disease Control and Prevention surveillance systems. We describe the Google Trends tool, explain how the data are processed, present examples, and discuss its strengths and limitations. Google Trends shows great promise as a timely, robust, and sensitive surveillance system. It is best used for surveillance of epidemics and diseases with high prevalences and is currently better suited to track disease activity in developed countries, because to be most effective, it requires large populations of Web search users. Spikes in search volume are currently hard to interpret but have the benefit of increasing vigilance. Google should work with public health care practitioners to develop specialized tools, using Google Flu Trends as a blueprint, to track infectious diseases. Suitable Web search query proxies for diseases need to be established for specialized tools or syndromic surveillance. This unique and innovative technology takes us one step closer to true real-time outbreak surveillance.
3.3 A 0.5-to-32.75Gb/s flexible-reach wireline transceiver in 20nm CMOS

The introduction of high-speed backplane transceivers inside FPGAs has addressed critical issues such as the ease in scalability of performance, high availability, flexible architectures, the use of standards, and rapid time to market. These have been crucial to address the ever-increasing demand for bandwidth in communication and storage systems [1-3], requiring novel techniques in receiver (RX) and clocking circuits.
Supervised Learning of Places from Range Data using AdaBoost

This paper addresses the problem of classifying places in the environment of a mobile robot into semantic categories. We believe that semantic information about the type of place improves the capabilities of a mobile robot in various domains including localization, path-planning, or human-robot interaction. Our approach uses AdaBoost, a supervised learning algorithm, to train a set of classifiers for place recognition based on laser range data. In this paper we describe how this approach can be applied to distinguish between rooms, corridors, doorways, and hallways. Experimental results obtained in simulation and with real robots demonstrate the effectiveness of our approach in various environments.
From linear story generation to branching story graphs

Narrative intelligence refers to the ability - human or computer - to organize experience into narrative. Recently, researchers have applied narrative intelligence to create interactive narrative systems, virtual worlds in which a story unfolds and the user is considered a character in the story, able to interact with elements and other characters in the virtual world. The standard approach to incorporating storytelling into a computer system is to script a story at design time. However, this approach limits the computer system's ability to adapt to the user's preferences and abilities. The alternative approach is to generate stories dynamically or on a per-session basis (one story per time the system is engaged). Narrative generation is a process that involves the selection, ordering, and presentation through discourse of narrative content. A system that can generate stories can adapt narrative to the user's preferences and abilities, has expanded replay value, and can interact with users in ways that system designers didn't initially envision
Health behavior models in the age of mobile interventions: are our theories up to the task?

Mobile technologies are being used to deliver health behavior interventions. The study aims to determine how health behavior theories are applied to mobile interventions. This is a review of the theoretical basis and interactivity of mobile health behavior interventions. Many of the mobile health behavior interventions reviewed were predominately one way (i.e., mostly data input or informational output), but some have leveraged mobile technologies to provide just-in-time, interactive, and adaptive interventions. Most smoking and weight loss studies reported a theoretical basis for the mobile intervention, but most of the adherence and disease management studies did not. Mobile health behavior intervention development could benefit from greater application of health behavior theories. Current theories, however, appear inadequate to inform mobile intervention development as these interventions become more interactive and adaptive. Dynamic feedback system theories of health behavior can be developed utilizing longitudinal data from mobile devices and control systems engineering models.
Assessing the effects of building social intelligence in a robotic interface for the home

This paper reports an exploration of the concept of social intelligence in the context of designing home dialogue systems for an Ambient Intelligence home. It describes a Wizard of Oz experiment involving a robotic interface capable of simulating several human social behaviours. Our results show that endowing a home dialogue system with some social intelligence will: (a) create a positive bias in the user’s perception of technology in the home environment, (b) enhance user acceptance for the home dialogue system, and (c) trigger social behaviours by the user in relation to the home dialogue system. q 2005 Elsevier B.V. All rights reserved.
Performance modeling from software components

When software products are assembled from pre-defined components, performance prediction should be based on the components also. This supports rapid model-building, using previously calibrated sub-models or "performance components", in sync with the construction of the product. The specification of a performance component must be tied closely to the software component specification, but it also includes performance related parameters (describing workload characteristics and demands), and it abstracts the behaviour of the component in various ways (for reasons related to practical factors in performance analysis). A useful set of abstractions and parameters are already defined for layered performance modeling. This work extends them to accommodate software components, using a new XML-based language called Component-Based Modeling Language (CBML). With CBML, compatible components can be inserted into slots provided in a hierarchical component specification based on the UML component model.
The Compact Circularly-Polarized Hollow Rectangular Dielectric Resonator Antenna With an Underlaid Quadrature Coupler

The wideband circularly polarized (CP) dielectric resonator antenna (DRA) is investigated with an underlaid quadrature coupler. The idea is used to realize a CP hollow rectangular DRA. Since the coupler is located beneath the DRA, it does not increase the footprint of the antenna, making the system very compact. The underlaid coupler is placed entirely inside the hollow region of the DRA and, thus, it can be designed easily as if there is no overlaid DRA. Two configurations are considered in this paper. In the first configuration, an external 50-Ω load is used for the matching port of the coupler. For the second one, a strip loaded by the DRA is used to provide a load and, thus, no lumped elements are required in this configuration. In this paper, a network model is also given to aid engineers in designing the proposed integrated DRA. The reflection coefficient, axial ratio, antenna gain, and radiation pattern for each configuration are studied. It was found that wide impedance and axial-ratio bandwidths can be obtained with the proposed CP DRAs. Measurements were carried out to verify the simulations, and reasonable agreement between them was obtained.