nils-herrmann/clean_scirepeval huggingface dataset:
NameClarifier: A Visual Analytics System for Author Name Disambiguation

In this paper, we present a novel visual analytics system called NameClarifier to interactively disambiguate author names in publications by keeping humans in the loop. Specifically, NameClarifier quantifies and visualizes the similarities between ambiguous names and those that have been confirmed in digital libraries. The similarities are calculated using three key factors, namely, co-authorships, publication venues, and temporal information. Our system estimates all possible allocations, and then provides visual cues to users to help them validate every ambiguous case. By looping users in the disambiguation process, our system can achieve more reliable results than general data mining models for highly ambiguous cases. In addition, once an ambiguous case is resolved, the result is instantly added back to our system and serves as additional cues for all the remaining unidentified names. In this way, we open up the black box in traditional disambiguation processes, and help intuitively and comprehensively explain why the corresponding classifications should hold. We conducted two use cases and an expert review to demonstrate the effectiveness of NameClarifier.
Bleu: a Method for Automatic Evaluation of Machine Translation

Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused. We propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run. We present this method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent
Synergistic Analysis of Evolving Graphs

Evolving graph processing involves repeating analyses, which are often iterative, over multiple snapshots of the graph corresponding to different points in time. Since the snapshots of an evolving graph share a great number of vertices and edges, traditional approaches that process these snapshots one at a time without exploiting this overlap contain much wasted effort on both data loading and computation, making them extremely inefficient. In this article, we identify major sources of inefficiencies and present two optimization techniques to address them. First, we propose a technique for amortizing the fetch cost by merging fetching of values for different snapshots of the same vertex. Second, we propose a technique for amortizing the processing cost by feeding values computed by earlier snapshots into later snapshots. We have implemented these optimizations in two distributed graph processing systems, namely, GraphLab and ASPIRE. Our experiments with multiple real evolving graphs and algorithms show that, on average fetch amortization speeds up execution of GraphLab and ASPIRE by 5.2× and 4.1× , respectively. Amortizing the processing cost yields additional average speedups of 2× and 7.9×, respectively.
A multi-method simulation of a high-frequency bus line

A methodology to model high-frequency bus lines is proposed and a realistic event-simulation model for such a line in the Netherlands is presented. This simulation model helps policy makers to predict changes that have to be made to bus routes and planned travel times before problems occur. With this model, different passenger growth scenarios can be easily evaluated. The model is validated using different key performance indicators, showing that under some model assumptions, it can realistically simulate real-life scenarios. The simulations workings are illustrated by a case study of passenger growth.
A Hybrid Deep Learning Architecture for Sentiment Analysis

In this paper, we propose a novel hybrid deep learning architecture which is highly efficient for sentiment analysis in resource-poor languages. We learn sentiment embedded vectors from the Convolutional Neural Network (CNN). These are augmented to a set of optimized features selected through a multi-objective optimization (MOO) framework. The sentiment augmented optimized vector obtained at the end is used for the training of SVM for sentiment classification.We evaluate our proposed approach for coarse-grained (i.e. sentence level) as well as fine-grained (i.e. aspect level) sentiment analysis on four Hindi datasets covering varying domains. In order to show that our proposed method is generic in nature, we also evaluate it on two benchmark English datasets. Evaluation shows that performance of the proposed method are consistent across all the datasets and often outperform the state-of-art systems. To the best of our knowledge, this is the very first attempt where such a deep learning model is used for sentiment analysis in less-resourced languages such as Hindi.
Design Patterns: Elements of Reusable Object-Oriented Software

Design Patterns is a modern classic in the literature of object-oriented development, offering timeless and elegant solutions to common problems in software design. It describes patterns for managing object creation, composing objects into larger structures, and coordinating control flow between objects. The book provides numerous examples where using composition rather than inheritance can improve the reusability and flexibility of code. Note, though, that its not a tutorial but a catalog that you can use to find an object-oriented design pattern thats appropriate for the needs of your particular application--a selection for virtuoso programmers who appreciate (or require) consistent, well-engineered object-oriented designs.
Moral development, executive functioning, peak experiences and brain patterns in professional and amateur classical musicians: Interpreted in light of a Unified Theory of Performance

This study compared professional and amateur classical musicians matched for age, gender, and education on reaction times during the Stroop color-word test, brainwaves during an auditory ERP task and during paired reaction-time tasks, responses on the Gibbs Sociomoral Reflection questionnaire, and self-reported frequencies of peak experiences. Professional musicians were characterized by: (1) lower color-word interference effects (Stroop task), (2) faster categorization of rare expected stimuli (P3b), and a trend for faster processing of rare unexpected stimuli (P3a), (3) higher scores on the Sociomoral Reflection questionnaire, and (4) more frequent peak experiences during rest, tasks, and sleep. Both groups had high values on the Brain Integration Scale. These findings are interpreted in light of a Unified Theory of Performance, which posits that effectiveness in any area is influenced by one's level of mind-brain development-emotional, cognitive, moral, ego and cortical development-with higher mind-brain development supporting greater effectiveness in any domain.
Feature Extraction from Product Reviews using Feature Similarity and Polarity

Research on developing techniques to access user generated content, and specifically user reviews on different products, came in the focus of the information research community in recent past. In particular, this paper addresses the problem of extracting the features from user comments of a particular product, taking advantage of a corpus with a semistructured format: pros, cons and summary. In this paper we propose a technique to extract a set of features based on user generated pros and cons for a particular product. Then using this set we test a feature similarity function to obtain new features from reviews (both from the pros/cons and from the free text summary) of the same and other products. Our experimental results have shown interesting conclusions.
Implicit theories of intelligence predict achievement across an adolescent transition: a longitudinal study and an intervention.

Two studies explored the role of implicit theories of intelligence in adolescents' mathematics achievement. In Study 1 with 373 7th graders, the belief that intelligence is malleable (incremental theory) predicted an upward trajectory in grades over the two years of junior high school, while a belief that intelligence is fixed (entity theory) predicted a flat trajectory. A mediational model including learning goals, positive beliefs about effort, and causal attributions and strategies was tested. In Study 2, an intervention teaching an incremental theory to 7th graders (N=48) promoted positive change in classroom motivation, compared with a control group (N=43). Simultaneously, students in the control group displayed a continuing downward trajectory in grades, while this decline was reversed for students in the experimental group.
FuseMe: Classification of sMRI images by fusion of Deep CNNs in 2D+&epsiv; projections

The methods of Content-Based visual information indexing and retrieval penetrate into Healthcare and become popular in Computer-Aided Diagnosis. Multimedia in medical images means different imaging modalities, but also multiple views of the same physiological object, such as human brain. In this paper we propose1 a multi-projection fusion approach with CNNs for diagnostics of Alzheimer Disease. Instead of working with the whole brain volume, it fuses CNNs from each brain projection sagittal, coronal, and axial ingesting a 2D+ϵ limited volume we have previously proposed. Three binary classification tasks are considered separating Alzheimer Disease (AD) patients from Mild Cognitive Impairment (MCI) and Normal control Subject (NC). Two fusion methods on FC-layer and on the single-projection CNN output show better performances, up to 91% and show competitive results with the SOA using heavier algorithmic chains.
A TOR-based anonymous communication approach to secure smart home appliances

Digital information has become a social infrastructure and with the expansion of the Internet, network infrastructure has become an indispensable part of social life and industrial activity for mankind. The idea of using existing electronics in smart home appliances and connecting them to the Internet is a new dimension along which technologies continue to grow, and in recent years mankind has witnessed an upsurge of usage of devices such as smart phone, smart television, home health-care device, smart LED light bulbs system, etc. Their build-in internet-controlled function has made them quite attractive to many segments of consumers and smart phone has become a common gadget for social networking. There are, however, serious challenges which need to be addressed as these tiny devices are designed for specific functions and lack processing capacity required for most security software. This research explores how these internet-enabled smart devices can be turned into very dangerous spots for distributed attacks purposes by cybercriminals for various ill intensions in a pinpointed manner. It then introduces a new approach to deal with such problems by taking advantage of the anonymous communication of the Onion Router (hereafter: TOR). It compares pros and cons of using anonymous communication scheme and justifies it be an efficient countermeasure to most attack scenarios.
Disability studies as a source of critical inquiry for the field of assistive technology

Disability studies and assistive technology are two related fields that have long shared common goals - understanding the experience of disability and identifying and addressing relevant issues. Despite these common goals, there are some important differences in what professionals in these fields consider problems, perhaps related to the lack of connection between the fields. To help bridge this gap, we review some of the key literature in disability studies. We present case studies of two research projects in assistive technology and discuss how the field of disability studies influenced that work, led us to identify new or different problems relevant to the field of assistive technology, and helped us to think in new ways about the research process and its impact on the experiences of individuals who live with disability. We also discuss how the field of disability studies has influenced our teaching and highlight some of the key publications and publication venues from which our community may want to draw more deeply in the future.
Open Information Extraction with Meta-pattern Discovery in Biomedical Literature

Biomedical open information extraction (BioOpenIE) is a novel paradigm to automatically extract structured information from unstructured text with no or little supervision. It does not require any pre-specified relation types but aims to extract all the relation tuples from the corpus. A major challenge for open information extraction (OpenIE) is that it produces massive surface-name formed relation tuples that cannot be directly used for downstream applications. We propose a novel framework CPIE (Clause+Pattern-guided Information Extraction) that incorporates clause extraction and meta-pattern discovery to extract structured relation tuples with little supervision. Compared with previous OpenIE methods, CPIE produces massive but more structured output that can be directly used for downstream applications. We first detect short clauses from input sentences. Then we extract quality textual patterns and perform synonymous pattern grouping to identify relation types. Last, we obtain the corresponding relation tuples by matching each quality pattern in the text. Experiments show that CPIE achieves the highest precision in comparison with state-of-the-art OpenIE baselines, and also keeps the distinctiveness and simplicity of the extracted relation tuples. CPIE shows great potential in effectively dealing with real-world biomedical literature with complicated sentence structures and rich information.
A Subsequence Interleaving Model for Sequential Pattern Mining

Recent sequential pattern mining methods have used the minimum description length (MDL) principle to define an encoding scheme which describes an algorithm for mining the most compressing patterns in a database. We present a novel subsequence interleaving model based on a probabilistic model of the sequence database, which allows us to search for the most compressing set of patterns without designing a specific encoding scheme. Our proposed algorithm is able to efficiently mine the most relevant sequential patterns and rank them using an associated measure of interestingness. The efficient inference in our model is a direct result of our use of a structural expectation-maximization framework, in which the expectation-step takes the form of a submodular optimization problem subject to a coverage constraint. We show on both synthetic and real world datasets that our model mines a set of sequential patterns with low spuriousness and redundancy, high interpretability and usefulness in real-world applications. Furthermore, we demonstrate that the quality of the patterns from our approach is comparable to, if not better than, existing state of the art sequential pattern mining algorithms.
Mitigating DoS attacks against signature-based authentication in VANETs

Vehicular ad hoc networks (VANETs) are supposed to improve traffic safety and drivers' experiences. In a typical VANET, a vehicle broadcasts safety messages to its neighbors. Since behaviors based on safety messages could be life-critical, authentication of these messages must be guaranteed. Many signature-based schemes have been proposed for authentication in VANETs, but few of them have addressed the problem of denial of service (DoS) attacks against signature-based authentication. In such a DoS attack, attackers can broadcast forged messages with invalid signatures to force the receiving vehicles to perform lots of unnecessary signature verifications, and thus the benign vehicles cannot verify the messages from other legitimate vehicles. Our scheme features a pre-authentication process before signature verifying process to deal with this kind of DoS attack. The pre-authentication process takes advantage of the one-way hash chain and a group rekeying scheme. Evaluations show that the proposed scheme mitigates such DoS attacks effectively.
SLR-Tool - A Tool for Performing Systematic Literature Reviews

Systematic literature reviews (SLRs) have been gaining a significant amount of attention from Software Engineering researchers since 2004. SLRs are considered to be a new research methodology in Software Engineering, which allow evidence to be gathered with regard to the usefulness or effectiveness of the technology proposed in Software Engineering for the development and maintenance of software products. This is demonstrated by the growing number of publications related to SLRs that have appeared in recent years. While some tools exist that can support some or all of the activities of the SLR processes defined in (Kitchenham & Charters, 2007), these are not free. The objective of this paper is to present the SLR-Tool, which is a free tool and is available on the following website: http://alarcosj.esi.uclm.es/SLRTool/, to be used by researchers from any discipline, and not only Software Engineering. SLR-Tool not only supports the process of performing SLRs proposed in (Kitchenham & Charters, 2007), but also provides additional functionalities such as: refining searches within the documents by applying text mining techniques; defining a classification schema in order to facilitate data synthesis; exporting the results obtained to the format of tables and charts; and exporting the references from the primary studies to the formats used in bibliographic packages such as EndNote, BibTeX or Ris. This tool has, to date, been used by members of the Alarcos Research Group and PhD students, and their perception of it is that it is both highly necessary and useful. Our purpose now is to circulate the use of SLR-Tool throughout the entire research community in order to obtain feedback from other users.
The Sorcerer's Apprentice Guide to Fault Attacks

The effect of faults on electronic systems has been studied since the 1970s when it was noticed that radioactive particles caused errors in chips. This led to further research on the effect of charged particles on silicon, motivated by the aerospace industry, which was becoming concerned about the effect of faults in airborne electronic systems. Since then various mechanisms for fault creation and propagation have been discovered and researched. This paper covers the various methods that can be used to induce faults in semiconductors and exploit such errors maliciously. Several examples of attacks stemming from the exploiting of faults are explained. Finally a series of countermeasures to thwart these attacks are described.
Understanding physiological responses to stressors during physical activity

With advances in physiological sensors, we are able to understand people's physiological status and recognize stress to provide beneficial services. Despite the great potential in physiological stress recognition, there are some critical issues that need to be addressed such as the sensitivity and variability of physiology to many factors other than stress (e.g., physical activity). To resolve these issues, in this paper, we focus on the understanding of physiological responses to both stressor and physical activity and perform stress recognition, particularly in situations having multiple stimuli: physical activity and stressors. We construct stress models that correspond to individual situations, and we validate our stress modeling in the presence of physical activity. Analysis of our experiments provides an understanding on how physiological responses change with different stressors and how physical activity confounds stress recognition with physiological responses. In both objective and subjective settings, the accuracy of stress recognition drops by more than 14% when physical activity is performed. However, by modularizing stress models with respect to physical activity, we can recognize stress with accuracies of 82% (objective stress) and 87% (subjective stress), achieving more than a 5-10% improvement from approaches that do not take physical activity into account.
Detectability thresholds and optimal algorithms for community structure in dynamic networks

We study the fundamental limits on learning latent community structure in dynamic networks. Specifically, we study dynamic stochastic block models where nodes change their community membership over time, but where edges are generated independently at each time step. In this setting (which is a special case of several existing models), we are able to derive the detectability threshold exactly, as a function of the rate of change and the strength of the communities. Below this threshold, we claim that no algorithm can identify the communities better than chance. We then give two algorithms that are optimal in the sense that they succeed all the way down to this limit. The first uses belief propagation (BP), which gives asymptotically optimal accuracy, and the second is a fast spectral clustering algorithm, based on linearizing the BP equations. We verify our analytic and algorithmic results via numerical simulation, and close with a brief discussion of extensions and open questions.
Progressive lossless compression of arbitrary simplicial complexes

Efficient algorithms for compressing geometric data have been widely developed in the recent years, but they are mainly designed for closed polyhedral surfaces which are manifold or "nearly manifold". We propose here a progressive geometry compression scheme which can handle manifold models as well as "triangle soups" and 3D tetrahedral meshes. The method is lossless when the decompression is complete which is extremely important in some domains such as medical or finite element.While most existing methods enumerate the vertices of the mesh in an order depending on the connectivity, we use a kd-tree technique [Devillers and Gandoin 2000] which does not depend on the connectivity. Then we compute a compatible sequence of meshes which can be encoded using edge expansion [Hoppe et al. 1993] and vertex split [Popović and Hoppe 1997].The main contributions of this paper are: the idea of using the kd-tree encoding of the geometry to drive the construction of a sequence of meshes, an improved coding of the edge expansion and vertex split since the vertices to split are implicitly defined, a prediction scheme which reduces the code for simplices incident to the split vertex, and a new generalization of the edge expansion operation to tetrahedral meshes.
Cyber Security Resource Allocation: A Markov Decision Process Approach

An effective defense-in-depth in cyber security applies multiple layers of defense throughout a system. The goalis to defend a system against cyber-attack using severalindependent methods. Therefore, a cyber-attack that is able to penetrate one layer of defense may be unsuccessful in other layers. Common layers of cyber defense include: attack avoidance, prevention, detection, survivability and recovery. It follows that in security-conscious organizations, the cyber security investment portfolio is divided into different layers of defense. For instance, a two-way division is agility and recovery. Cyber agility pursues attack avoidance techniques such that cyber-attacks are rendered as ineffective, whereas cyber recovery seeks to fight-through successful attacks. We show that even when the primary focus is on the agility of a system, recovery should be an essential point during implementation because the frequency of attacks will degrade the system and a quick and fast recovery is necessary. However, there is not yet an optimum mechanism to allocate limited cyber security resourcesinto the different layers. We propose an approach using theMarkov Decision Process (MDP) framework for resourcesallocation between the two end layers: agility and recovery.