nils-herrmann/clean_scirepeval huggingface dataset:
Dynamics of Platform Competition: Exploring the Role of Installed Base, Platform Quality and Consumer Expectations

This paper seeks to answer three questions. First, which drives the success of a platform, installed base, platform quality or consumer expectations? Second, when does a monopoly emerge in a platform-based market? Finally, when is a platform-based market socially efficient? We analyze a dynamic model where an entrant with superior quality competes with an incumbent platform, and examine long-run market outcomes. We find that the answers to these questions depend critically on two parameters: the strength of indirect network effects and consumers’ discount factor of future applications. In addition, contrary to the popular belief that indirect network effects protect incumbents and are the source of market inefficiency, we find that under certain conditions, indirect network effects could enhance entrants’ quality advantage and market outcomes hence could be more efficient with stronger indirect network effects. We empirically examine the competition between the Xbox and PlayStation 2 consoles. We find that Xbox has a small quality advantage over PlayStation 2. In addition, the strength of indirect network effects and consumers’ discount factor in this market are within the range in which platform success is driven by quality advantage and the market is potentially efficient. Counterfactual experiments suggest that PlayStation 2 could have driven Xbox out of the market had the strength of indirect network effects more than doubled or had consumers’ discount factor increased by fifty percent.
Multi-task Learning of Facial Landmarks and Expression

Recently, deep neural networks have been shown to perform competitively on the task of predicting facial expression from images. Trained by gradient-based methods, these networks are amenable to "multi-task" learning via a multiple term objective. In this paper we demonstrate that learning representations to predict the position and shape of facial landmarks can improve expression recognition from images. We show competitive results on two large-scale datasets, the ICML 2013 Facial Expression Recognition challenge, and the Toronto Face Database.
Deep Cross-Domain Fashion Recommendation

With the increasing number of online shopping services, the number of users and the quantity of visual and textual information on the Internet, there is a pressing need for intelligent recommendation systems that analyze the user's behavior amongst multiple domains and help them to find the desirable information without the burden of search. However, there is little research that has been done on complex recommendation scenarios that involve knowledge transfer across multiple domains. This problem is especially challenging when the involved data sources are complex in terms of the limitations on the quantity and quality of data that can be crawled. The goal of this paper is studying the connection between visual and textual inputs for better analysis of a certain domain, and to examine the possibility of knowledge transfer from complex domains for the purpose of efficient recommendations. The methods employed to achieve this study include both design of architecture and algorithms using deep learning technologies to analyze the effect of deep pixel-wise semantic segmentation and text integration on the quality of recommendations. We plan to develop a practical testing environment in a fashion domain.
Overview of Automotive Sensors

An up-to-date review paper on automotive sensors is presented. Attention is focused on sensors used in production automotive systems. The primary sensor technologies in use today are reviewed and are classified according to their three major areas ofautomotive systems application–powertrain, chassis, and body.  As described in this paper, for use in automotive systems, there are six types of rotational motion sensors, four types of pressure sensors, five types of position sensors, and three types of temperature sensors. Additionally, two types of mass air flow sensors, five types of exhaust gas oxygen sensors, one type of engine knock sensor, four types of linear acceleration sensors, four types of angular-rate sensors, four types of occupant comfort/convenience sensors, two types of near-distance obstacle detection sensors, four types of far-distance obstacle detection sensors, and and ten types of emerging, state-of the-art, sensors technologies are identified.
A comparative analysis of Spearman's rho and Kendall's tau in normal and contaminated normal models

This paper analyzes the performances of Spearman’s rho (SR) and Kendall’s tau (KT) with respect to samples drawn from bivariate normal and contaminated normal populations. Theoretical and simulation results suggest that, contrary to the opinion of equivalence between SR and KT in some literature, the behaviors of SR and KT are strikingly different in the aspects of bias effect, variance, mean square error (MSE), and asymptotic relative efficiency (ARE). The new findings revealed in this work provide not only deeper insights into the two most widely used rank-based correlation coefficients, but also a guidance for choosing which one to use under the circumstances where Pearson’s product moment correlation coefficient (PPMCC) fails to apply. & 2012 Elsevier B.V. All rights reserved.
A Vector Space Modeling Approach to Spoken Language Identification

We propose a novel approach to automatic spoken language identification (LID) based on vector space modeling (VSM). It is assumed that the overall sound characteristics of all spoken languages can be covered by a universal collection of acoustic units, which can be characterized by the acoustic segment models (ASMs). A spoken utterance is then decoded into a sequence of ASM units. The ASM framework furthers the idea of language-independent phone models for LID by introducing an unsupervised learning procedure to circumvent the need for phonetic transcription. Analogous to representing a text document as a term vector, we convert a spoken utterance into a feature vector with its attributes representing the co-occurrence statistics of the acoustic units. As such, we can build a vector space classifier for LID. The proposed VSM approach leads to a discriminative classifier backend, which is demonstrated to give superior performance over likelihood-based n-gram language modeling (LM) backend for long utterances. We evaluated the proposed VSM framework on 1996 and 2003 NIST Language Recognition Evaluation (LRE) databases, achieving an equal error rate (EER) of 2.75% and 4.02% in the 1996 and 2003 LRE 30-s tasks, respectively, which represents one of the best results reported on these popular tasks
An Efficient Features - Based License Plate Localization Method

This paper presents a feature-based license plate localization algorithm that copes with multi-object problem in different image capturing conditions. The proposed algorithm is robust against illumination, shadow, scale, rotation, and weather condition. It extracts license plate candidates using edge statistics and morphological operations and removes the incorrect candidates according to the determined features of license plates. We have formed a rather complete database of 269 images in different conditions. The proposed algorithm successfully detected the accurate location of the license plates in 96.5% cases, which outperforms the other available approaches in the literature
An Interactive Method for Inferring Demographic Attributes in Twitter

Twitter data offers an unprecedented opportunity to study demographic differences in public opinion across a virtually unlimited range of subjects. Whilst demographic attributes are often implied within user data, they are not always easily identified using computational methods. In this paper, we present a semi-automatic solution that combines automatic classification methods with a user interface designed to enable rapid resolution of ambiguous cases. TweetClass employs a two-step, interactive process to support the determination of gender and age attributes. At each step, the user is presented with feedback on the confidence levels of the automated analysis and can choose to refine ambiguous cases by examining key profile and content data. We describe how a user-centered design approach was used to optimise the interface and present the results of an evaluation which suggests that TweetClass can be used to rapidly boost demographic sample sizes in situations where high accuracy is required.
Learning Spatial and Temporal Cues for Multi-Label Facial Action Unit Detection

Facial action units (AU) are the fundamental units to decode human facial expressions. At least three aspects affect performance of automated AU detection: spatial representation, temporal modeling, and AU correlation. Unlike most studies that tackle these aspects separately, we propose a hybrid network architecture to jointly model them. Specifically, spatial representations are extracted by a Convolutional Neural Network (CNN), which, as analyzed in this paper, is able to reduce person-specific biases caused by hand-crafted descriptors (e.g., HOG and Gabor). To model temporal dependencies, Long Short-Term Memory (LSTMs) are stacked on top of these representations, regardless of the lengths of input videos. The outputs of CNNs and LSTMs are further aggregated into a fusion network to produce per-frame prediction of 12 AUs. Our network naturally addresses the three issues together, and yields superior performance compared to existing methods that consider these issues independently. Extensive experiments were conducted on two large spontaneous datasets, GFT and BP4D, with more than 400,000 frames coded with 12 AUs. On both datasets, we report improvements over a standard multi-label CNN and feature-based state-of-the-art. Finally, we provide visualization of the learned AU models, which, to our best knowledge, reveal how machines see AUs for the first time.
An ultra-wideband microwave balun using a tapered coaxial coil structure working from kHz range to beyond 26.5 GHz

Many wideband baluns have been presented in the past using coupled lines, pure magnetic coupling or slotlines. Their limitations were set whether in high frequency or low frequency performance. Due to their lumped element bandpass representation, many of them allow just certain bandwidth. The tapered coaxial coil structure allows balun operation beyond 26 GHz and down to the kHz range through partial ferrite filling. The cable losses, cable cut-off frequency, the number of windings, the permeability of the ferrite and the minimum coil diameter limit the bandwidth. The tapering allows resonance free operation through the whole band. Many microwave devices like mixers, power amplifiers, SWR-bridges, antennas, etc. can be made more broadband with this kind of balun. A stepwise approach to the proposed structure is presented and compared to previous balun implementations. Finally, a measurement is provided and some implementation possibilities are discussed.
Simulating Affective Touch: Using a Vibrotactile Array to Generate Pleasant Stroking Sensations

Gentle stroking touches are rated most pleasant when applied at a velocity of between 1-10 cm/s. Such touches are considered highly relevant in social interactions. Here, we investigate whether stroking sensations generated by a vibrotactile array can produce similar pleasantness responses, with the ultimate goal of using this type of haptic display in technology mediated social touch. A study was conducted in which participants received vibrotactile stroking stimuli of different velocities and intensities, applied to their lower arm. Results showed that the stimuli were perceived as continuous stroking sensations in a straight line. Furthermore, pleasantness ratings for low intensity vibrotactile stroking followed an inverted U-curve, similar to that found in research into actual stroking touches. The implications of these findings are discussed.
Analyzing the energy efficiency of a database server

Rising energy costs in large data centers are driving an agenda for energy-efficient computing. In this paper, we focus on the role of database software in affecting, and, ultimately, improving the energy efficiency of a server. We first characterize the power-use profiles of database operators under different configuration parameters. We find that common database operations can exercise the full dynamic power range of a server, and that the CPU power consumption of different operators, for the same CPU utilization, can differ by as much as 60%. We also find that for these operations CPU power does not vary linearly with CPU utilization.
 We then experiment with several classes of database systems and storage managers, varying parameters that span from different query plans to compression algorithms and from physical layout to CPU frequency and operating system scheduling. Contrary to what recent work has suggested, we find that within a single node intended for use in scale-out (shared-nothing) architectures, the most energy-efficient configuration is typically the highest performing one. We explain under which circumstances this is not the case, and argue that these circumstances do not warrant a retargeting of database system optimization goals. Further, our results reveal opportunities for cross-node energy optimizations and point out directions for new scale-out architectures.
A secure address resolution protocol

We propose an architecture for securely resolving IP addresses into hardware addresses over an Ethernet. The proposed architecture consists of a secure server connected to the Ethernet and two protocols: an invite-accept protocol and a request-reply protocol. Each computer connected to the Ethernet can use the invite-accept protocol to periodically record its IP address and its hardware address in the database of the secure server. Each computer can later use the requestreply protocol to obtain the hardware address of any other computer connected to the Ethernet from the database of the secure server. These two protocols are designed to overcome the actions of any adversary that can lose sent messages, arbitrarily modify the fields of sent messages, and replay old messages.
RenderGAN: Generating Realistic Labeled Data

Deep Convolutional Neuronal Networks (DCNNs) are showing remarkable performance on many computer vision tasks. Due to their large parameter space, they require many labeled samples when trained in a supervised setting. The costs of annotating data manually can render the use of DCNNs infeasible. We present a novel framework called RenderGAN that can generate large amounts of realistic, labeled images by combining a 3D model and the Generative Adversarial Network framework. In our approach, image augmentations (e.g., lighting, background, and detail) are learned from unlabeled data such that the generated images are strikingly realistic while preserving the labels known from the 3D model. We apply the RenderGAN framework to generate images of barcode-like markers that are attached to honeybees. Training a DCNN on data generated by the RenderGAN yields considerably better performance than training it on various baselines.
Analyzing Intention in Utterances

This paper describes a model of cooperative behavior and describes how such a model can be applied in a natural language understanding system. We assume that agents attempt to recognize the plans of other agents and, then, use this plan when deciding what response to make. In particular, we show that, given a setting in which purposeful dialogues occur, this model can account for responses that provide more information that explicitly requested and for appropriate responses to both short sentence fragments and indirect speech acts.
OpenBTS: A Step Forward in the Cognitive Direction

The paper proposes the integration of new cognitive capabilities within the well known OpenBTS architecture in order to make the system able to react in a smart way to the changes of the radio channel. In particular, the proposed spectrum sensing strategy allows the OpenBTS system to be aware of other active transmissions by forcing to choose a new radio channel, within the GSM frequency band, when a licensed primary user has to transmit on a busy channel. The implemented scheme, representing a solid step forward in the cognitive direction, has been validated throughout a detailed testbed pointing out strengths and limitations in realistic communication environments.
A Data Mining Ontology for Grid Programming

The Grid is an integrated infrastructure for coordinated resource sharing and problem solving in distributed environments. The effective and efficient use of stored data and its transformation into information and knowledge will be a main driver in Grid evolution. The use of ontologies to describe Grid resources will simplify and structure the systematic building of Grid applications through the composition and reuse of software components and the development of knowledge-based services and tools. The paper presents an ontology for the Data Mining domain that can be used to simplify the development of distributed knowledge discovery applications on the Grid, offering to a domain expert a reference model for the different kind of data mining tasks, methodologies and software available to solve a given problem, helping a user in finding the most appropriate solution. How the DAMON ontology is used to enhance the design of distributed data mining applications on the KNOWLEDGE GRID is also shown.
Coverage Path Planning: The Boustrophedon Cellular Decomposition

Coverage path planning is the determination of a path that a robot must take in order to pass over each point in an environment. Applications include vacuuming, oor scrubbing, and inspection. We developed the boustrophedon cellular decomposition, which is an exact cellular decomposition approach, for the purposes of coverage. Each cell in the boustrophedon is covered with simple back and forth motions. Once each cell is covered, then the entire environment is covered. Therefore, coverage is reduced to nding an exhaustive path through a graph which represents the adjacency relationships of the cells in the boustrophedon decomposition. This approach is provably complete and Experiments on a mobile robot validate this approach.
Trust and Reputation Systems

There are currently very few practical methods for assessin g the quality of resources or the reliability of other entities in the o nline environment. This makes it difficult to make decisions about which resources ca n be relied upon and which entities it is safe to interact with. Trust and repu tation systems are aimed at solving this problem by enabling service consumers to eliably assess the quality of services and the reliability of entities befo r they decide to use a particular service or to interact with or depend on a given en tity. Such systems should also allow serious service providers and online play ers to correctly represent the reliability of themselves and the quality of thei r s rvices. In the case of reputation systems, the basic idea is to let parties rate e ch other, for example after the completion of a transaction, and use the aggreg ated ratings about a given party to derive its reputation score. In the case of tru st systems, the basic idea is to analyse and combine paths and networks of trust rel ationships in order to derive measures of trustworthiness of specific nodes. Rep utation scores and trust measures can assist other parties in deciding whether or not to transact with a given party in the future, and whether it is safe to depend on a given resource or entity. This represents an incentive for good behaviour and for offering reliable resources, which thereby tends to have a positive effect on t he quality of online markets and communities. This chapter describes the backgr ound, current status and future trend of online trust and reputation systems.
Mindfulness-based cognitive therapy for generalized anxiety disorder.

While cognitive behavior therapy has been found to be effective in the treatment of generalized anxiety disorder (GAD), a significant percentage of patients struggle with residual symptoms. There is some conceptual basis for suggesting that cultivation of mindfulness may be helpful for people with GAD. Mindfulness-based cognitive therapy (MBCT) is a group treatment derived from mindfulness-based stress reduction (MBSR) developed by Jon Kabat-Zinn and colleagues. MBSR uses training in mindfulness meditation as the core of the program. MBCT incorporates cognitive strategies and has been found effective in reducing relapse in patients with major depression (Teasdale, J. D., Segal, Z. V., Williams, J. M. G., Ridgeway, V., Soulsby, J., & Lau, M. (2000). Prevention of relapse/recurrence in major depression by mindfulness-based cognitive therapy. Journal of Consulting and Clinical Psychology, 6, 615-623).



Eligible subjects recruited to a major academic medical center participated in the group MBCT course and completed measures of anxiety, worry, depressive symptoms, mood states and mindful awareness in everyday life at baseline and end of treatment.



Eleven subjects (six female and five male) with a mean age of 49 (range=36-72) met criteria and completed the study. There were significant reductions in anxiety and depressive symptoms from baseline to end of treatment.



MBCT may be an acceptable and potentially effective treatment for reducing anxiety and mood symptoms and increasing awareness of everyday experiences in patients with GAD. Future directions include development of a randomized clinical trial of MBCT for GAD.
Efficient Network Coding Signatures in the Standard Model

Network Coding is a routing technique where each node may actively modify the received packets before transmitting them. While this departure from passive networks improves throughput and resilience to packet loss it renders transmission susceptible to pollution attacks where nodes can misbehave and change in a malicious way the messages transmitted. Nodes cannot use standard signature schemes to authenticate the modified packets: this would require knowledge of the original sender’s signing key. Network coding signature schemes offer a cryptographic solution to this problem. Very roughly, such signatures allow signing vector spaces (or rather bases of such spaces). Furthermore, these signatures are homomorphic: given signatures on a set of vectors it is possible to create signatures for any linear combination of these vectors. Designing such schemes is a difficult task, and the few existent constructions either rely on random oracles or are rather inefficient. In this paper we introduce two new network coding signature schemes. Both of our schemes are provably secure in the standard model, rely on standard assumptions, and are in the same efficiency class with previous solutions based on random oracles.
A note on the evaluation of generative models

Probabilistic generative models can be used for compression, denoising, inpainting, texture synthesis, semi-supervised learning, unsupervised feature learning, and other tasks. Given this wide range of applications, it is not surprising that a lot of heterogeneity exists in the way these models are formulated, trained, and evaluated. As a consequence, direct comparison between models is often difficult. This article reviews mostly known but often underappreciated properties relating to the evaluation and interpretation of generative models with a focus on image models. In particular, we show that three of the currently most commonly used criteria—average log-likelihood, Parzen window estimates, and visual fidelity of samples—are largely independent of each other when the data is high-dimensional. Good performance with respect to one criterion therefore need not imply good performance with respect to the other criteria. Our results show that extrapolation from one criterion to another is not warranted and generative models need to be evaluated directly with respect to the application(s) they were intended for. In addition, we provide examples demonstrating that Parzen window estimates should generally be avoided.