nils-herrmann/clean_scirepeval huggingface dataset:
Feature Extraction and Selection for Emotion Recognition from EEG

Emotion recognition from EEG signals allows the direct assessment of the “inner” state of a user, which is considered an important factor in human-machine-interaction. Many methods for feature extraction have been studied and the selection of both appropriate features and electrode locations is usually based on neuro-scientific findings. Their suitability for emotion recognition, however, has been tested using a small amount of distinct feature sets and on different, usually small data sets. A major limitation is that no systematic comparison of features exists. Therefore, we review feature extraction methods for emotion recognition from EEG based on 33 studies. An experiment is conducted comparing these features using machine learning techniques for feature selection on a self recorded data set. Results are presented with respect to performance of different feature selection methods, usage of selected feature types, and selection of electrode locations. Features selected by multivariate methods slightly outperform univariate methods. Advanced feature extraction techniques are found to have advantages over commonly used spectral power bands. Results also suggest preference to locations over parietal and centro-parietal lobes.
The biology and evolution of music: A comparative perspective

Studies of the biology of music (as of language) are highly interdisciplinary and demand the integration of diverse strands of evidence. In this paper, I present a comparative perspective on the biology and evolution of music, stressing the value of comparisons both with human language, and with those animal communication systems traditionally termed "song". A comparison of the "design features" of music with those of language reveals substantial overlap, along with some important differences. Most of these differences appear to stem from semantic, rather than structural, factors, suggesting a shared formal core of music and language. I next review various animal communication systems that appear related to human music, either by analogy (bird and whale "song") or potential homology (great ape bimanual drumming). A crucial comparative distinction is between learned, complex signals (like language, music and birdsong) and unlearned signals (like laughter, ape calls, or bird calls). While human vocalizations clearly build upon an acoustic and emotional foundation shared with other primates and mammals, vocal learning has evolved independently in our species since our divergence with chimpanzees. The convergent evolution of vocal learning in other species offers a powerful window into psychological and neural constraints influencing the evolution of complex signaling systems (including both song and speech), while ape drumming presents a fascinating potential homology with human instrumental music. I next discuss the archeological data relevant to music evolution, concluding on the basis of prehistoric bone flutes that instrumental music is at least 40,000 years old, and perhaps much older. I end with a brief review of adaptive functions proposed for music, concluding that no one selective force (e.g., sexual selection) is adequate to explaining all aspects of human music. I suggest that questions about the past function of music are unlikely to be answered definitively and are thus a poor choice as a research focus for biomusicology. In contrast, a comparative approach to music promises rich dividends for our future understanding of the biology and evolution of music.
Multithreaded Hybrid Feature Tracking for Markerless Augmented Reality

We describe a novel markerless camera tracking approach and user interaction methodology for augmented reality (AR) on unprepared tabletop environments. We propose a real-time system architecture that combines two types of feature tracking. Distinctive image features of the scene are detected and tracked frame-to-frame by computing optical flow. In order to achieve real-time performance, multiple operations are processed in a synchronized multi-threaded manner: capturing a video frame, tracking features using optical flow, detecting distinctive invariant features, and rendering an output frame. We also introduce user interaction methodology for establishing a global coordinate system and for placing virtual objects in the AR environment by tracking a user's outstretched hand and estimating a camera pose relative to it. We evaluate the speed and accuracy of our hybrid feature tracking approach, and demonstrate a proof-of-concept application for enabling AR in unprepared tabletop environments, using bare hands for interaction.
Generative Visual Manipulation on the Natural Image Manifold

Interactive Image Generation User edits Generated images User edits Generated images User edits Generated images  Zhu et al. Learning a Discriminative Model for the Perception of Realism in Composite Images. ICCV 2015.  Goodfellow et al.   Radford et al. Unsupervised representation learning with deep convolutional generative adversarial networks.  Natural images 0, I , Unif 1, 1
Meaning Of A Tag: A collaborative approach to bridge the gap between tagging and Linked Data

This paper introduces MOAT, a lightweight Semantic Web framework that provides a collaborative way to let Web 2.0 content producers give meanings to their tags in a machinereadable way. To achieve this goal, this approach relies on Linked Data principles, using URIs from existing resources to define these meanings. That way, users can create interlinked RDF data and let their content enter the Semantic Web, while solving some limits of free-tagging at the same time.
SLA-Based Resource Allocation for Software as a Service Provider (SaaS) in Cloud Computing Environments

Cloud computing has been considered as a solution for solving enterprise application distribution and configuration challenges in the traditional software sales model. Migrating from traditional software to Cloud enables on-going revenue for software providers. However, in order to deliver hosted services to customers, SaaS companies have to either maintain their own hardware or rent it from infrastructure providers. This requirement means that SaaS providers will incur extra costs. In order to minimize the cost of resources, it is also important to satisfy a minimum service level to customers. Therefore, this paper proposes resource allocation algorithms for SaaS providers who want to minimize infrastructure cost and SLA violations. Our proposed algorithms are designed in a way to ensure that Saas providers are able to manage the dynamic change of customers, mapping customer requests to infrastructure level parameters and handling heterogeneity of Virtual Machines. We take into account the customers' Quality of Service parameters such as response time, and infrastructure level parameters such as service initiation time. This paper also presents an extensive evaluation study to analyze and demonstrate that our proposed algorithms minimize the SaaS provider's cost and the number of SLA violations in a dynamic resource sharing Cloud environment.
Data-Driven Sparse Structure Selection for Deep Neural Networks

Deep convolutional neural networks have liberated its extraordinary power on various tasks. However, it is still very challenging to deploy stateof-the-art models into real-world applications due to their high computational complexity. How can we design a compact and effective network without massive experiments and expert knowledge? In this paper, we propose a simple and effective framework to learn and prune deep models in an end-to-end manner. In our framework, a new type of parameter – scaling factor is first introduced to scale the outputs of specific structures, such as neurons, groups or residual blocks. Then we add sparsity regularizations on these factors, and solve this optimization problem by a modified stochastic Accelerated Proximal Gradient (APG) method. By forcing some of the factors to zero, we can safely remove the corresponding structures, thus prune the unimportant parts of a CNN. Comparing with other structure selection methods that may need thousands of trials or iterative fine-tuning, our method is trained fully end-to-end in one training pass without bells and whistles. We evaluate our method, Sparse Structure Selection with several state-of-the-art CNNs, and demonstrate very promising results with adaptive depth and width selection. Code is available at: https://github.com/huangzehao/ sparse-structure-selection.
Compressing Neural Networks with the Hashing Trick

As deep nets are increasingly used in applications suited for mobile devices, a fundamental dilemma becomes apparent: the trend in deep learning is to grow models to absorb everincreasing data set sizes; however mobile devices are designed with very little memory and cannot store such large models. We present a novel network architecture, HashedNets, that exploits inherent redundancy in neural networks to achieve drastic reductions in model sizes. HashedNets uses a low-cost hash function to randomly group connection weights into hash buckets, and all connections within the same hash bucket share a single parameter value. These parameters are tuned to adjust to the HashedNets weight sharing architecture with standard backprop during training. Our hashing procedure introduces no additional memory overhead, and we demonstrate on several benchmark data sets that HashedNets shrink the storage requirements of neural networks substantially while mostly preserving generalization performance.
Position-normalized click prediction in search advertising

Click-through rate (CTR) prediction plays a central role in search advertising. One needs CTR estimates unbiased by positional effect in order for ad ranking, allocation, and pricing to be based upon ad relevance or quality in terms of click propensity. However, the observed click-through data has been confounded by positional bias, that is, users tend to click more on ads shown in higher positions than lower ones, regardless of the ad relevance. We describe a probabilistic factor model as a general principled approach to studying these exogenous and often overwhelming phenomena. The model is simple and linear in nature, while empirically justified by the advertising domain. Our experimental results with artificial and real-world sponsored search data show the soundness of the underlying model assumption, which in turn yields superior prediction accuracy.
Cache based recurrent neural network language model inference for first pass speech recognition

Recurrent neural network language models (RNNLMs) have recently produced improvements on language processing tasks ranging from machine translation to word tagging and speech recognition. To date, however, the computational expense of RNNLMs has hampered their application to first pass decoding. In this paper, we show that by restricting the RNNLM calls to those words that receive a reasonable score according to a n-gram model, and by deploying a set of caches, we can reduce the cost of using an RNNLM in the first pass to that of using an additional n-gram model. We compare this scheme to lattice rescoring, and find that they produce comparable results for a Bing Voice search task. The best performance results from rescoring a lattice that is itself created with a RNNLM in the first pass.
METHODOLOGIES FOR ONTOLOGY DEVELOPMENT

It is now widely recognised that constructing a domain model, or ontology, is an important step in the development of knowledge based systems. What is lacking, however, is a clear understanding of how to build ontologies. In this paper we survey the work which has been done so far in beginning to provide a methodology for building ontologies. This work is still formative, and relies heavily on particular experiences. We also provide some discussion of this work, and identify the key issues that must be addressed if we are to move on from ontology construction being an art and to make it an understood engineering process.
A Review on the Portuguese Enterprises Web Accessibility Levels - A Website Accessibility High Level Improvement Proposal

Click here and insert your abstract text. The Web accessibility issue has been subject of study for a wide number of organizations all around the World. The current paper describes an accessibility evaluation that aimed to test the Portuguese enterprises websites. Has the presented results state, the evaluated websites accessibility levels are significantly bad, but the majority of the detected errors are not very complex from a technological point-of-view. With this is mind, our research team, in collaboration with a Portuguese enterprise named ANO and the support of its UTAD-ANOgov/PEPPOL research project, elaborated an improvement proposal, directed to the Web content developers, which aimed on helping these specialists to better understand and implement Web accessibility features.  Selection and peer-review under responsibility of the Scientific Programme Committee of the 5th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion (DSAI 2013).
A Dual-Broadband, Dual-Polarized Base Station Antenna for 2G/3G/4G Applications

A base station antenna with dual-broadband and dual-polarization characteristics is presented in this letter. The proposed antenna contains four parts: a lower-band element, an upper-band element, arc-shaped baffle plates, and a box-shaped reflector. The lower-band element consists of two pairs of dipoles with additional branches for bandwidth enhancement. The upper-band element embraces two crossed hollow dipoles and is nested inside the lower-band element. Four arc-shaped baffle plates are symmetrically arranged on the reflector for isolating the lower- and upper-band elements and improving the radiation performance of upper-band element. As a result, the antenna can achieve a bandwidth of 50.6% for the lower band and 48.2% for the upper band when the return loss is larger than 15 dB, fully covering the frequency ranges 704–960 and 1710–2690 MHz for 2G/3G/4G applications. Measured port isolation larger than 27.5 dB in both the lower and upper bands is also obtained. At last, an array that consists of two lower-band elements and five upper-band elements is discussed for giving an insight into the future array design.
Age-related differences in walking stability.

a large proportion of falls in older people occur when walking; however the mechanisms underlying impaired balance during gait are poorly understood.



to evaluate acceleration patterns at the head and pelvis in young and older subjects when walking on a level and an irregular walking surface, in order to develop an understanding of how ageing affects postural responses to challenging walking conditions.



temporo-spatial gait parameters and variables derived from acceleration signals were recorded in 30 young people aged 22-39 years (mean 29.0, SD 4.3), and 30 older people with a low risk of falling aged 75-85 years (mean 79.0, SD 3.0) while walking on a level and an irregular walking surface. Subjects also underwent tests of vision, sensation, strength, reaction time and balance.



older subjects exhibited a more conservative gait pattern, characterised by reduced velocity, shorter step length and increased step timing variability. These differences were particularly pronounced when walking on the irregular surface. The magnitude of accelerations at the head and pelvis were generally smaller in older subjects; however the smoothness of the acceleration signals did not differ between the two groups. Older subjects performed worse on tests of vision, peripheral sensation, strength, reaction time and balance.



the adoption of a more conservative basic gait pattern by older people with a low risk of falling reduces the magnitude of accelerations experienced by the head and pelvis when walking, which is likely to be a compensatory strategy to maintain balance in the presence of age-related deficits in physiological function, particularly reduced lower limb strength.
A random-key genetic algorithm for the generalized traveling salesman problem

The Generalized Traveling Salesman Problem is a variation of the well-known Traveling Salesman Problem in which the set of nodes is divided into clusters; the objective is to find a minimum-cost tour passing through one node from each cluster. We present an effective heuristic for this problem. The method combines a genetic algorithm (GA) with a local tour improvement heuristic. Solutions are encoded using random keys, which circumvent the feasibility problems encountered when using traditional GA encodings. On a set of 41 standard test problems with up to 442 nodes, the heuristic found solutions that were optimal in most cases and were within 1% of optimality in all but the largest problems, with computation times generally within 10 seconds for the smaller problems and a few minutes for the larger ones. The heuristic outperforms all other heuristics published to date in both solution quality and computation time.
Greening Geographical Load Balancing

Energy expenditure has become a significant fraction of data center operating costs. Recently, "geographical load balancing" has been proposed to reduce energy cost by exploiting the electricity price differences across regions. However, this reduction of cost can paradoxically increase total energy use. We explore whether the geographical diversity of Internet-scale systems can also provide environmental gains. Specifically, we explore whether geographical load balancing can encourage use of "green" renewable energy and reduce use of "brown" fossil fuel energy. We make two contributions. First, we derive three distributed algorithms for achieving optimal geographical load balancing. Second, we show that if the price of electricity is proportional to the instantaneous fraction of the total energy that is brown, then geographical load balancing significantly reduces brown energy use. However, the benefits depend strongly on dynamic energy pricing and the form of pricing used.
Classifying Computer Generated Charts

We present an approach for classifying images of charts based on the shape and spatial relationships of their primitives. Five categories are considered: bar-charts, curve-plots, pie-charts, scatter-plots and surface-plots. We introduce two novel features to represent the structural information based on (a) region segmentation and (b) curve saliency. The local shape is characterized using the Histograms of Oriented Gradients (HOG) and the Scale Invariant Feature Transform (SIFT) descriptors. Each image is represented by sets of feature vectors of each modality. The similarity between two images is measured by the overlap in the distribution of the features -measured using the Pyramid Match algorithm. A test image is classified based on its similarity with training images from the categories. The approach is tested with a database of images collected from the Internet.
A Color Similarity Measure for Robust Shadow Removal in Real Time

We introduce an approach for realtime segmentation of a scene into foreground objects, background, and object shadows superimposed on the background. To segment foreground objects, we use an adaptive thresholding method, which is able to deal with rapid changes of the overall brightness. The segmented image usually includes shadows cast by the objects onto the background. Our approach is able to robustly remove the shadow from the background while preserving the silhouette of the foreground object. We discuss a similarity measure for comparing color pixels, which improves the quality of shadow removal significantly. As the image segmentation is part of a real-time interaction environment, real-time processing is needed. Our implementation allows foreground segmentation and robust shadow removal with 15 Hz.
Photon: fault-tolerant and scalable joining of continuous data streams

Web-based enterprises process events generated by millions of users interacting with their websites. Rich statistical data distilled from combining such interactions in near real-time generates enormous business value. In this paper, we describe the architecture of Photon, a geographically distributed system for joining multiple continuously flowing streams of data in real-time with high scalability and low latency, where the streams may be unordered or delayed. The system fully tolerates infrastructure degradation and datacenter-level outages without any manual intervention. Photon guarantees that there will be no duplicates in the joined output (at-most-once semantics) at any point in time, that most joinable events will be present in the output in real-time (near-exact semantics), and exactly-once semantics eventually.
 Photon is deployed within Google Advertising System to join data streams such as web search queries and user clicks on advertisements. It produces joined logs that are used to derive key business metrics, including billing for advertisers. Our production deployment processes millions of events per minute at peak with an average end-to-end latency of less than 10 seconds. We also present challenges and solutions in maintaining large persistent state across geographically distant locations, and highlight the design principles that emerged from our experience.
UNDERSTANDING CONSUMERS ATTITUDE TOWARD ADVERTISING

Literature in advertising and information systems suggests that advertising in both traditional media and the Internet is either easily ignored by the audience or is perceived with little value. However, these studies assumed that the audience was passive and failed to consider the motives of the users. In light of this, the present study measures consumers attitudes toward advertisements for different purposes/functions (brand building and directional) and different media (traditional and Internet-based). Literature suggests the following factors that contribute to consumers perceptions of ads: entertainment, irritation, informativeness, credibility, and demographic. We believe that interactivity is also a factor that contributes to consumers perceptions. By understanding consumers attitude towards advertising, designers and marketers can better strategize their advertising designs. A better understanding of interactivity can also help to improve the effectiveness of interactive media such as the Internet. A methodology for studying the factors that contribute to consumers perceptions of ads is proposed and implications for Internet-based advertising and e-commerce is discussed.