nils-herrmann/clean_scirepeval huggingface dataset:
Multiclass Support Vector Machines for EEG-Signals Classification

In this paper, we proposed the multiclass support vector machine (SVM) with the error-correcting output codes for the multiclass electroencephalogram (EEG) signals classification problem. The probabilistic neural network (PNN) and multilayer perceptron neural network were also tested and benchmarked for their performance on the classification of the EEG signals. Decision making was performed in two stages: feature extraction by computing the wavelet coefficients and the Lyapunov exponents and classification using the classifiers trained on the extracted features. The purpose was to determine an optimum classification scheme for this problem and also to infer clues about the extracted features. Our research demonstrated that the wavelet coefficients and the Lyapunov exponents are the features which well represent the EEG signals and the multiclass SVM and PNN trained on these features achieved high classification accuracies
Shape Analysis Using the Auto Diffusion Function

Scalar functions defined on manifold triangle meshes is a starting point for many geometry processing algorithms such as mesh parametrization, skeletonization, and segmentation. In this paper, we propose the Auto Diffusion Function (ADF) which is a linear combination of the eigenfunctions of the Laplace-Beltrami operator in a way that has a simple physical interpretation. The ADF of a given 3D object has a number of further desirable properties: Its extrema are generally at the tips of features of a given object, its gradients and level sets follow or encircle features, respectively, it is controlled by a single parameter which can be interpreted as feature scale, and, finally, the ADF is invariant to rigid and isometric deformations. We describe the ADF and its properties in detail and compare it to other choices of scalar functions on manifolds. As an example of an application, we present a pose invariant, hierarchical skeletonization and segmentation algorithm which makes direct use of the ADF.
NVMain: An Architectural-Level Main Memory Simulator for Emerging Non-volatile Memories

Emerging non-volatile memory (NVM) technologies, such as PCRAM and STT-RAM, have demonstrated great potentials to be the candidates as replacement for DRAM-based main memory design for computer systems. It is important for computer architects to model such emerging memory technologies at the architecture level, to understand the benefits and limitations for better utilizing them to improve the performance/energy/reliability of future computing systems. In this paper, we introduce an architectural-level simulator called NV Main, which can model main memory design with both DRAM and emerging non-volatile memory technologies, and can facilitate designers to perform design space explorations utilizing these emerging memory technologies. We discuss design points of the simulator and provide validation of the model, along with case studies on using the tool for design space explorations.
Experimental evidence of a template aging effect in iris biometrics

It has been widely accepted that iris biometric systems are not subject to a template aging effect. Baker et al.  recently presented the first published evidence of a template aging effect, using images acquired from 2004 through 2008 with an LG 2200 iris imaging system, representing a total of 13 subjects (26 irises). We report on a template aging study involving two different iris recognition algorithms, a larger number of subjects (43), a more modern imaging system (LG 4000), and over a shorter time-lapse (2 years). We also investigate the degree to which the template aging effect may be related to pupil dilation and/or contact lenses. We find evidence of a template aging effect, resulting in an increase in match hamming distance and false reject rate.
Making Sense of Work Life : A Structural Model of Burnout

Hospital-based nurses (N = 832) and doctors (N = 603) in northern and eastern Spain completed a survey of job burnout, areas of work life, and management issues. Analysis of the results provides support for a mediation model of burnout that depicts employees’ energy, involvement, and efficacy as intermediary experiences between their experiences of work life and their evaluations of organizational change. The key element of this model is its focus on employees’ capacity to influence their work environments toward greater conformity with their core values. The model considers 3 aspects of that capacity: decision-making participation, organizational justice, and supervisory relationships. The analysis supports this model and emphasizes a central role for first-line supervisors in employees’ experiences of work life.jasp_563 57..75
Convolutional Neural Networks for Direct Text Deblurring

In this work we address the problem of blind deconvolution and denoising. We focus on restoration of text documents and we show that this type of highly structured data can be successfully restored by a convolutional neural network. The networks are trained to reconstruct high-quality images directly from blurry inputs without assuming any specific blur and noise models. We demonstrate the performance of the convolutional networks on a large set of text documents and on a combination of realistic de-focus and camera shake blur kernels. On this artificial data, the convolutional networks significantly outperform existing blind deconvolution methods, including those optimized for text, in terms of image quality and OCR accuracy. In fact, the networks outperform even state-of-the-art non-blind methods for anything but the lowest noise levels. The approach is validated on real photos taken by various devices.
Ontology Learning from Text: An Overview

This volume brings together a collection of extended versions of selected papers from two workshops on ontology learning, knowledge acquisition and related topics that were organized in the context of the European Conference on Artificial Intelligence (ECAI) 2004 and the International Conference on Knowledge Engineering and Management (EKAW) 2004. The volume presents current research in ontology learning, addressing three perspectives: methodologies that have been proposed to automatically extract information from texts and to give a structured organization to such knowledge, including approaches based on machine learning techniques; evaluation methods for ontology learning, aiming at defining procedures and metrics for a quantitative evaluation of the ontology learning task; and finally application scenarios that make ontology learning a challenging area in the context of real applications such as bio-informatics. According to the three perspectives mentioned above, the book is divided into three sections, each including a selection of papers addressing respectively the methods, the applications and the evaluation of ontology learning approaches. However, all selected papers pay considerably attention to the evaluation perspective, as this was a central topic of the ECAI 2004 workshop out of which most of the papers in this volume originate.
The Knowledge Awakens: Keeping Knowledge Bases Fresh with Emerging Entities

Entity search over news, social media and the Web allows users to precisely retrieve concise information about specific people, organizations, movies and their characters, and other kinds of entities. This expressive search mode builds on two major assets: 1) a knowledge base (KB) that contains the entities of interest and 2) entity markup in the documents of interest derived by automatic disambiguation of entity names (NED) and linking names to the KB. These prerequisites are not easily available, though, in the important case when a user is interested in a newly emerging entity (EE) such as new movies, new songs, etc. Automatic methods for detecting and canonicalizing EEs are not nearly at the same level as the NED methods for prominent entities that have rich descriptions in the KB. To overcome this major limitation, we have developed an approach and prototype system that allows searching for EEs in a user-friendly manner. The approach leverages the human in the loop by prompting for user feedback on candidate entities and on characteristic keyphrases for EEs. For convenience and low burden on users, this process is supported by the automatic harvesting of tentative keyphrases. Our demo system shows this interactive process and its high usability.
Access Pattern disclosure on Searchable Encryption: Ramification, Attack and Mitigation

The advent of cloud computing has ushered in an era of mass data storage in remote servers. Remote data storage offers reduced data management overhead for data owners in a cost effective manner. Sensitive documents, however, need to be stored in encrypted format due to security concerns. But, encrypted storage makes it difficult to search on the stored documents. Therefore, this poses a major barrier towards selective retrieval of encrypted documents from the remote servers. Various protocols have been proposed for keyword search over encrypted data to address this issue. Most of the available protocols leak data access patterns due to efficiency reasons. Although, oblivious RAM based protocols can be used to hide data access patterns, such protocols are computationally intensive and do not scale well for real world datasets. In this paper, we introduce a novel attack that exploits data access pattern leakage to disclose significant amount of sensitive information using a modicum of prior knowledge. Our empirical analysis with a real world dataset shows that the proposed attack is able to disclose sensitive information with a very high accuracy. Additionally, we propose a simple technique to mitigate the risk against the proposed attack at the expense of a slight increment in computational resources and communication cost. Furthermore, our proposed mitigation technique is generic enough to be used in conjunction with any searchable encryption scheme that reveals data access pattern.
Predictors of Online Buying Behavior

Shopping there is far from universal, even for those already online for other tasks. To help identify the factors influencing online shopping, the Wharton Forum on Electronic Commerce (ecom.wharton.upenn.edu) sponsors an ongoing research project begun in 1997 called the Wharton Virtual Test Market (WVTM). It seeks to understand Web consumer demographics, attitudes about online shopping, and predictors of online buying behavior. The major difference between the 1998 survey we conducted of WVTM members and other surveys is that its focus was on buying on the Net, trying not just to describe, but to model, the relationship between individual Net users’ personal characteristics and their buying activity. The WVTM is a panel, most of whose members agreed to participate in further research. Although the 1998 survey established a baseline, we expect future research to examine differences in shopping behavior over time. The Wharton Forum on Electronic Commerce began recruiting a panel of Web users from around the world in October 1997. As part of the participant registration process, 10,180 people completed a survey asking 62 questions about online behavior and attitudes about Internet communication and privacy Consumers worldwide can shop online 24 hours a day, seven days a week, 365 days
Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging

Stacked autoencoders (SAEs), as part of the deep learning (DL) framework, have been recently proposed for feature extraction in hyperspectral remote sensing. With the help of hidden nodes in deep layers, a high-level abstraction is achieved for data reduction whilst maintaining the key information of the data. As hidden nodes in SAEs have to deal simultaneously with hundreds of features from hypercubes as inputs, this increases the complexity of the process and leads to limited abstraction and performance. As such, segmented SAE (S-SAE) is proposed by confronting the original features into smaller data segments, which are separately processed by different smaller SAEs. This has resulted in reduced complexity but improved efficacy of data abstraction and accuracy of data classification.
A global test for groups of genes: testing association with a clinical outcome

This paper presents a global test to be used for the analysis of microarray data. Using this test it can be determined whether the global expression pattern of a group of genes is significantly related to some clinical outcome of interest. Groups of genes may be any size from a single gene to all genes on the chip (e.g. known pathways, specific areas of the genome or clusters from a cluster analysis).



The test allows groups of genes of different size to be compared, because the test gives one p-value for the group, not a p-value for each gene. Researchers can use the test to investigate hypotheses based on theory or past research or to mine gene ontology databases for interesting pathways. Multiple testing problems do not occur unless many groups are tested. Special attention is given to visualizations of the test result, focussing on the associations between samples and showing the impact of individual genes on the test result.


AVAILABILITY
An R-package globaltest is available from http://www.bioconductor.org
MATLAB Based ECG Signal Classification

An electrocardiogram (ECG) is a bioelectrical signal which records the heart's electrical activity versus time. It is an important diagnostic tool for assessing heart functions. The early detection of arrhythmia is very important for the cardiac patients. ECG arrhythmia can be defined as any of a group of conditions in which the electrical activity of the heart is irregular and can cause heartbeat to be slow or fast. It can take place in a healthy heart and be of minimal consequence, but they may also indicate a serious problem that leads to stroke or sudden cardiac death. As ECG signal being non stationary signal, the arrhythmia may occur at random in the time-scale, which means, the arrhythmia symptoms may not show up all the time but would manifest at certain irregular intervals during the day. Thus, automatic classification of arrhythmia is critical in clinical cardiology, especially for the treatment of patients in the intensive care unit. This project implements a simulation tool on MATLAB platform to detect abnormalities in the ECG signal. The ECG signal is downloaded from MIT-BIH Arrhythmia database, since this signal contains some noise and artifacts hence pre-processing of ECG signal are performed first. The preprocessing of ECG signal is performed with help of Wavelet toolbox wherein baseline wandering, denoising and removal of high frequency and low frequency is performed to improve SNR ratio of ECG signal. The Wavelet toolbox is also used for feature extraction of ECG signal. Classification of arrhythmia is based on basic classification rules. The complete project is implemented on MATLAB platform. The performance of the algorithm is evaluated on MIT–BIH Database. The different types of arrhythmia classes including normal beat, Tachycardia, Bradycardia and Myocardial Infract (MI) are classified. KeywordsDb6 , feature extraction, arrhythmia.
On the Synergy of Network Science and Artificial Intelligence

A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
Honeyware: A Web-Based Low Interaction Client Honeypot

Modern attacks are being made against client side applications, such as web browsers, which most users use to surf and communicate on the internet. Client honeypots visit and interact with suspect web sites in order to detect and collect information about malware to protect users from malicious websites or to allow security professionals to investigate malicious content. This paper will present the idea of using web-based technology and integrating it with a client honeypot by building a low interaction client honeypot tool called Honeyware. It describes the benefits of Honeyware as well as the challenges of a low interaction client honeypot and provides some ideas for how these challenges could be overcome.
ASIFT: A New Framework for Fully Affine Invariant Image Comparison

If a physical object has a smooth or piecewise smooth boundary, its images obtained by cameras in varying positions undergo smooth apparent deformations. These deformations are locally well approximated by affine transforms of the image plane. In consequence the solid object recognition problem has often been led back to the computation of affine invariant image local features. Such invariant features could be obtained by normalization methods, but no fully affine normalization method exists for the time being. Even scale invariance is dealt with rigorously only by the scaleinvariant feature transform (SIFT) method. By simulating zooms out and normalizing translation and rotation, SIFT is invariant to four out of the six parameters of an affine transform. The method proposed in this paper, affine-SIFT (ASIFT), simulates all image views obtainable by varying the two camera axis orientation parameters, namely, the latitude and the longitude angles, left over by the SIFT method. Then it covers the other four parameters by using the SIFT method itself. The resulting method will be mathematically proved to be fully affine invariant. Against any prognosis, simulating all views depending on the two camera orientation parameters is feasible with no dramatic computational load. A two-resolution scheme further reduces the ASIFT complexity to about twice that of SIFT. A new notion, the transition tilt, measuring the amount of distortion from one view to another, is introduced. While an absolute tilt from a frontal to a slanted view exceeding 6 is rare, much higher transition tilts are common when two slanted views of an object are compared . The attainable transition tilt is measured for each affine image comparison method. The new method permits one to reliably identify features that have undergone transition tilts of large magnitude, up to 36 and higher. This fact is substantiated by many experiments which show that ASIFT significantly outperforms the state-of-the-art methods SIFT, maximally stable extremal region (MSER), Harris-affine, and Hessian-affine.
Tool-supported requirements prioritization: Comparing the AHP and CBRank methods

Requirements prioritization aims at identifying the most important requirements for a software system, a crucial step when planning for system releases and deciding which requirements to implement in each release. Several prioritization methods and supporting tools have been proposed so far. How to evaluate their properties, with the aim of supporting the selection of the most appropriate method for a specific project, is considered a relevant question. In this paper, we present an empirical study aiming at evaluating two state-of-the art tool-supported requirements prioritization methods, AHP and CBRank. We focus on three measures: the ease of use, the time-consumption and the accuracy. The experiment has been conducted with 23 experienced subjects on a set of 20 requirements from a real project. Results indicate that for the first two characteristics CBRank overcomes AHP, while for the accuracy AHP performs better than CBRank, even if the resulting ranks from the two methods are very similar. The majority of the users found CBRank the ‘‘overall best”
Towards a ROS-based autonomous cloud robotics platform for data center monitoring

Data center monitoring has been a critical subject of research in recent years. In this paper we present a robotic system, based on the Robot Operating System (ROS), in which a mobile robot equipped with a laser range sensor and an Inertial Motion Unit (IMU) is able to autonomously navigate in a data center room for accurate monitoring of critical measurements, such as servers' external temperature, humidity and other physical quantities. The robot is able to autonomously create a map of a previously unknown room, localize therein and execute a list of measurements at different locations, which are provided by the user via a web Graphical User Interface (GUI). The application is based on a cloud robotics infrastructure which encloses the ROS nodes and exposes REST APIs to the user. We discuss our implementation choices with regards to the particular requirements of the scenario, both in terms of robot navigation and software infrastructure, and present some preliminary results in a real scenario.
Perception driven texture generation

This paper investigates a novel task of generating texture images from perceptual descriptions. Previous work on texture generation focused on either synthesis from examples or generation from procedural models. Generating textures from perceptual attributes has not been extensively studied yet. Meanwhile, perceptual attributes, such as directionality, regularity and roughness are important factors for human observers to describe a texture. In this paper, we propose a joint deep network model that combines adversarial training and perceptual feature regression for texture generation, while only random noise and user-defined perceptual attributes are required as input. In this model, a preliminary trained convolutional neural network is essentially integrated with the adversarial framework, which can drive the generated textures to possess given perceptual attributes. An important aspect of the proposed model is that, if we change one of the input perceptual features, the corresponding appearance of the generated textures will also be changed. We designed several experiments to validate the effectiveness of the proposed method. The results show that the proposed method can produce high-quality texture images with desired perceptual properties.
Opinion mining from online hotel reviews - A text summarization approach

Online travel forums and social networks have become the most popular platform for sharing travel information, with enormous numbers of reviews posted daily. Automatically generated hotel summaries could aid travelers in selecting hotels. This study proposes a novel multi-text summarization technique for identifying the topk most informative sentences of hotel reviews. Previous studies on review summarization have primarily examined content analysis, which disregards critical factors like author credibility and conflicting opinions. We considered such factors and developed a new sentence importance metric. Both the content and sentiment similarities were used to determine the similarity of two sentences. To identify the topk sentences, the k -medoids clustering algorithm was used to partition sentences into k groups. The medoids from these groups were then selected as the final summarization results. To evaluate the performance of the proposed method, we collected two sets of reviews for the two hotels posted on TripAdvisor.com. A total of 20 subjects were invited to review the text summarization results from the proposed approach and two conventional approaches for the two hotels. The results indicate that the proposed approach outperforms the other two, and most of the subjects believed that the proposed approach can provide more comprehensive hotel information.