nils-herrmann/clean_scirepeval huggingface dataset:
Sinew: a SQL system for multi-structured data

As applications are becoming increasingly dynamic, the notion that a schema can be created in advance for an application and remain relatively stable is becoming increasingly unrealistic. This has pushed application developers away from traditional relational database systems and away from the SQL interface, despite their many well-established benefits. Instead, developers often prefer self-describing data models such as JSON, and NoSQL systems designed specifically for their relaxed semantics.
 In this paper, we discuss the design of a system that enables developers to continue to represent their data using self-describing formats without moving away from SQL and traditional relational database systems. Our system stores arbitrary documents of key-value pairs inside physical and virtual columns of a traditional relational database system, and adds a layer above the database system that automatically provides a dynamic relational view to the user against which fully standard SQL queries can be issued. We demonstrate that our design can achieve an order of magnitude improvement in performance over alternative solutions, including existing relational database JSON extensions, MongoDB, and shredding systems that store flattened key-value data inside a relational database.
AmbientSense: A real-time ambient sound recognition system for smartphones

This paper presents design, implementation, and evaluation of AmbientSense, a real-time ambient sound recognition system on a smartphone. AmbientSense continuously recognizes user context by analyzing ambient sounds sampled from a smartphone's microphone. The phone provides a user with realtime feedback on recognised context. AmbientSense is implemented as an Android app and works in two modes: in autonomous mode processing is performed on the smartphone only. In server mode recognition is done by transmitting audio features to a server and receiving classification results back. We evaluated both modes in a set of 23 daily life ambient sound classes and describe recognition performance, phone CPU load, and recognition delay. The application runs with a fully charged battery up to 13.75 h on a Samsung Galaxy SII smartphone and up to 12.87 h on a Google Nexus One phone. Runtime and CPU load were similar for autonomous and server modes.
Parsing Models for Identifying Multiword Expressions

Multiword expressions lie at the syntax/semantics interface and have motivated alternative theories of syntax like Construction Grammar. Until now, however, syntactic analysis and multiword expression identification have been modeled separately in natural language processing. We develop two structured prediction models for joint parsing and multiword expression identification. The first is based on context-free grammars and the second uses tree substitution grammars, a formalism that can store larger syntactic fragments. Our experiments show that both models can identify multiword expressions with much higher accuracy than a state-of-the-art system based on word co-occurrence statistics.We experiment with Arabic and French, which both have pervasive multiword expressions. Relative to English, they also have richer morphology, which induces lexical sparsity in finite corpora. To combat this sparsity, we develop a simple factored lexical representation for the context-free parsing model. Morphological analyses are automatically transformed into rich feature tags that are scored jointly with lexical items. This technique, which we call a factored lexicon, improves both standard parsing and multiword expression identification accuracy.
Porcelain versus composite inlays/onlays: effects of mechanical loads on stress distribution, adhesion, and crown flexure.

This study used 2-D finite element modeling to simulate cuspal flexure and stresses at the surface and tooth-restoration interface of a restored maxillary molar using three restorative materials; the influence of four inlay/onlay preparation configurations on stress distribution within the complex was also investigated. A buccolingual cross-section of an intact molar was digitized and used to create 2-D models restored with different restorative materials (feldspathic porcelain, high- and low-elastic modulus composites) and tooth preparations (small and large inlays, small and large onlays). Two simulated 25-N oblique loads were applied to the cusps. The tangential stress for each finite element node located at the tooth surface, interfacial stress, and relative cuspal flexure were analyzed. All materials and tooth preparations exhibited similar surface tangential stress patterns, with a definite compressive area at the external cusp ridges, a tensile zone at the occlusal surface, and compression stress peaks at the CEJ. The low-elastic modulus composite showed reduced tensile stresses at its surface but increased tension at the dentin-adhesive interface when compared to ceramics. All types of onlays demonstrated a majority of compressive interfacial stresses, while inlays showed a majority of tensile stresses. The interfacial tension at the dentin level increased with the flexibility of the restorative material. Only the large ceramic onlay displayed almost pure compression at the interface. Composite-restored teeth exhibited increased crown flexure, while porcelain-restored teeth showed increased crown stiffness. Porcelain inlays/onlays featured more detrimental stresses at the occlusal surface but better potential protection against debonding at the dentin-restoration interface compared to composite inlays/onlays. Ceramic onlays/overlays seem to represent an effective answer to restore severely damaged posterior teeth.
Ethical Principles for Socially Assistive Robotics

The health, education, and other service applications for robots that assist through primarily social rather than physical interaction are rapidly growing, and so is the research into such technologies. Socially assistive robotics (SAR) aims to address critical areas and gaps in care by automating supervision, coaching, motivation, and companionship aspects of one-on-one interactions with individuals from various large and growing populations, including stroke survivors, the elderly and individuals with dementia, children with autism spectrum disorders, among many others. In this way, roboticists hope to improve the standard of care for large user groups. Naturally, SAR systems pose several ethical challenges regarding their design, implementation, and deployment. This paper examines the ethical challenges of socially assistive robotics from three points of view (user, caregiver, peer) using core principles from medical ethics (autonomy, beneficence, non-maleficence, justice) to determine how intended and unintended effects of a SAR can impact the delivery of care.
An integrated architecture for autonomous vehicles simulation

Modeling and simulation tools are being increasingly acclaimed in the research field of autonomous vehicles systems, as they provide suitable test beds for the development and evaluation of such complex systems. However, these tools still do not account for some integration capabilities amongst several state-of-the-art Intelligent Transportation Systems, e.g. to study autonomous driving behaviors in human-steered urban traffic scenarios, which are crucial to the Future Urban Transport paradigm.
 In this paper we describe the modeling and implementation of an integration architecture of two types of simulators, namely a robotics and a traffic simulator. This integration should enable autonomous vehicles to be deployed in a rather realistic traffic flow as an agent entity (on the traffic simulator), at the same time it simulates all its sensors and actuators (on the robotics counterpart). Also, the statistical tools available in the traffic simulator will allow practitioners to infer what kind of advantages such a novel technology will bring to our everyday's lives. Furthermore, an architecture for the integration of the aforementioned simulators is proposed and implemented in the light of the most desired features of such software environments.
 To assess the usefulness of the platform architecture towards the expected realistic simulation facility, a comprehensive system evaluation is performed and critically reviewed, leveraging the feasibility of the integration. Further developments and future perspectives are also suggested.
Language classification from bilingual word embedding graphs

We study the role of the second language in bilingual word embeddings in monolingual semantic evaluation tasks. We find strongly and weakly positive correlations between down-stream task performance and second language similarity to the target language. Additionally, we show how bilingual word embeddings can be employed for the task of semantic language classification and that joint semantic spaces vary in meaningful ways across second languages. Our results support the hypothesis that semantic language similarity is influenced by both structural similarity as well as geography/contact.
From research to practice: experiences engineering a production metadata database for a scale out file system

HP’s StoreAll with Express Query is a scalable commercial file archiving product that offers sophisticated file metadata management and search capabilities . A new REST API enables fast, efficient searching to find all files that meet a given set of metadata criteria and the ability to tag files with custom metadata fields. The product brings together two significant systems: a scale out file system and a metadata database based on LazyBase [10]. In designing and building the combined product, we identified several real-world issues in using a pipelined database system in a distributed environment, and overcame several interesting design challenges that were not contemplated by the original research prototype. This paper highlights our experiences.
Novel Zero-Voltage and Zero-Current-Switching Full Bridge PWM Converter Using Transformer Auxiliary Winding

A novel zero voltage and zero current switching (ZVZCS) full bridge (FB) pulse width modulation (PWM) converter is proposed to improve the demerits of the previously presented ZVZCS-FB-PWM converters [5]–[8], such as, use of lossy components or additional active switches. A simple auxiliary circuit which includes neither lossy components nor active switches provides ZVZCS conditions to primary switches, ZVS for leading-leg switches and ZCS for lagging-leg switches. Many advantages including simple circuit topology, high efficiency, and low cost make the new converter attractive for high power ( 2 kW) applications. The operation, analysis, features and design considerations are illustrated and verified on a 2.5 kW, 100 kHz insulated gate bipolar transistor (IGBT) based experimental circuit.
Software quality and agile methods

Agile methods may produce software faster but we also need to know how they meet our quality requirements. In this paper we compare the waterfall model with agile processes to show how agile methods achieve software quality under time pressure and in an unstable requirements environment, i.e. we analyze agile software quality assurance. We present a detailed waterfall model showing its software quality support processes. We then show the quality practices that agile methods have integrated into their processes. This allows us to answer the question "can agile methods ensure quality even though they develop software faster and can handle unstable requirements?".
Assessment of multi-hop interpersonal trust in social networks by Three-Valued Subjective Logic

Assessing multi-hop interpersonal trust in online social networks (OSNs) is critical for many social network applications such as online marketing but challenging due to the difficulties of handling complex OSN topology, in existing models such as subjective logic, and the lack of effective validation methods. To address these challenges, we for the first time properly define trust propagation and combination in arbitrary OSN topologies by proposing 3VSL (Three-Valued Subjective Logic). The 3VSL distinguishes the posteriori and priori uncertainties existing in trust, and the difference between distorting and original opinions, thus be able to compute multi-hop trusts in arbitrary graphs. We theoretically proved the capability based on the Dirichlet distribution. Furthermore, an online survey system is implemented to collect interpersonal trust data and validate the correctness and accuracy of 3VSL in real world. Both experimental and numerical results show that 3VSL is accurate in computing interpersonal trust in OSNs.
Finding Topic Words for Hierarchical Summarization

Hierarchies have long been used for organization, summarization, and access to information. In this paper we define summarization in terms of a probabilistic language model and use the definition to explore a new technique for automatically generating topic hierarchies by applying a graph-theoretic algorithm, which is an approximation of the Dominating Set Problem. The algorithm efficiently chooses terms according to a language model. We compare the new technique to previous methods proposed for constructing topic hierarchies including subsumption and lexical hierarchies, as well as the top TF.IDF terms. Our results show that the new technique consistently performs as well as or better than these other techniques. They also show the usefulness of hierarchies compared with a list of terms.
A linear regression model for the analysis of life times.

A linear model is suggested for the influence of covariates on the intensity function. This approach is less vulnerable than the Cox model to problems of inconsistency when covariates are deleted or the precision of covariate measurements is changed. A method of non-parametric estimation of regression functions is presented. This results in plots that may give information on the change over time in the influence of covariates. A test method and two goodness of fit plots are also given. The approach is illustrated by simulation as well as by data from a clinical trial of treatment of carcinoma of the oropharynx.
Events on the edge

The emergence of large-scale receptor-based systems has enabled applications to execute complex business logic over data generated from monitoring the physical world. An important functionality required by these applications is the detection and response to complex events, often in real-time. Bridging the gap between low-level receptor technology and such high-level needs of applications remains a significant challenge.We demonstrate our solution to this problem in the context of HiFi, a system we are building to solve the data management problems of large-scale receptor-based systems. Specifically, we show how HiFi generates simple events out of receptor data at its edges and provides high-functionality complex event processing mechanisms for sophisticated event detection using a real-world library scenario.
Object Calibration for Augmented Reality

Augmented reality involves the use of models and their associated renderings to supplement information in a real scene. In order for this information to be relevant or meaningful, the models must be positioned and displayed in such a way that they align with their corresponding real objects. For practical reasons this alignment cannot be known a priori, and cannot be hard-wired into a system. Instead a simple, reliable alignment or calibration process is performed so that computer models can be accurately registered with their real-life counterparts. We describe the design and implementation of such a process and we show how it can be used to create convincing interactions between real and virtual objects.
Mendeley - A Last.fm For Research?

This paper aims to explore how the principles of a well-known Web 2.0 service, the world¿s largest social music service "Last.fm" (www.last.fm), can be applied to research, which potential it could have in the world of research (e.g. an open and interdisciplinary database, usage-based reputation metrics, and collaborative filtering) and which challenges such a model would face in academia. A real-world application of these principles, "Mendeley" (www.mendeley.com), will be demoed at the IEEE e-Science Conference 2008.
Deep Learning Based Feature Selection for Remote Sensing Scene Classification

With the popular use of high-resolution satellite images, more and more research efforts have been placed on remote sensing scene classification/recognition. In scene classification, effective feature selection can significantly boost the final performance. In this letter, a novel deep-learning-based feature-selection method is proposed, which formulates the feature-selection problem as a feature reconstruction problem. Note that the popular deep-learning technique, i.e., the deep belief network (DBN), achieves feature abstraction by minimizing the reconstruction error over the whole feature set, and features with smaller reconstruction errors would hold more feature intrinsics for image representation. Therefore, the proposed method selects features that are more reconstructible as the discriminative features. Specifically, an iterative algorithm is developed to adapt the DBN to produce the inquired reconstruction weights. In the experiments, 2800 remote sensing scene images of seven categories are collected for performance evaluation. Experimental results demonstrate the effectiveness of the proposed method.
Safe software updates via multi-version execution

Software systems are constantly evolving, with new versions and patches being released on a continuous basis. Unfortunately, software updates present a high risk, with many releases introducing new bugs and security vulnerabilities. 
 We tackle this problem using a simple but effective multi-version based approach. Whenever a new update becomes available, instead of upgrading the software to the new version, we run the new version in parallel with the old one; by carefully coordinating their executions and selecting the behaviour of the more reliable version when they diverge, we create a more secure and dependable multi-version application. 
 We implemented this technique in Mx, a system targeting Linux applications running on multi-core processors, and show that it can be applied successfully to several real applications such as Coreutils, a set of user-level UNIX applications; Lighttpd, a popular web server used by several high-traffic websites such as Wikipedia and YouTube; and Redis, an advanced key-value data structure server used by many well-known services such as GitHub and Flickr.
Effective Detection of SQL/XPath Injection Vulnerabilities in Web Services

This paper proposes a new automatic approach for the detection of SQL Injection and XPath Injection vulnerabilities, two of the most common and most critical types of vulnerabilities in web services. Although there are tools that allow testing web applications against security vulnerabilities, previous research shows that the effectiveness of those tools in web services environments is very poor. In our approach a representative workload is used to exercise the web service and a large set of SQL/XPath Injection attacks are applied to disclose vulnerabilities. Vulnerabilities are detected by comparing the structure of the SQL/XPath commands issued in the presence of attacks to the ones previously learned when running the workload in the absence of attacks. Experimental evaluation shows that our approach performs much better than known tools (including commercial ones), achieving extremely high detection coverage while maintaining the false positives rate very low.
Hit Song Science Is Not Yet a Science

We describe a large-scale experiment aiming at validating the hypothesis that the popularity of music titles can be predicted from global acoustic or human features. We use a 32.000 title database with 632 manually-entered labels per title including 3 related to the popularity of the title. Our experiment uses two audio feature sets, as well as the set of all the manually-entered labels but the popularity ones. The experiment shows that some subjective labels may indeed be reasonably well-learned by these techniques, but not popularity. This contradicts recent and sustained claims made in the MIR community and in the media about the existence of “Hit Song Science”.
Predicting activity attendance in event-based social networks: content, context and social influence

The newly emerging event-based social networks (EBSNs) connect online and offline social interactions, offering a great opportunity to understand behaviors in the cyber-physical space. While existing efforts have mainly focused on investigating user behaviors in traditional social network services (SNS), this paper aims to exploit individual behaviors in EBSNs, which remains an unsolved problem. In particular, our method predicts activity attendance by discovering a set of factors that connect the physical and cyber spaces and influence individual's attendance of activities in EBSNs. These factors, including content preference, context (spatial and temporal) and social influence, are extracted using different models and techniques. We further propose a novel Singular Value Decomposition with Multi-Factor Neighborhood (SVD-MFN) algorithm to predict activity attendance by integrating the discovered heterogeneous factors into a single framework, in which these factors are fused through a neighborhood set. Experiments based on real-world data from Douban Events demonstrate that the proposed SVD-MFN algorithm outperforms the state-of-the-art prediction methods.
Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors

A brain-computer interface (BCI) is a communication system that can help users interact with the outside environment by translating brain signals into machine commands. The use of electroencephalographic (EEG) signals has become the most common approach for a BCI because of their usability and strong reliability. Many EEG-based BCI devices have been developed with traditional wet- or micro-electro-mechanical-system (MEMS)-type EEG sensors. However, those traditional sensors have uncomfortable disadvantage and require conductive gel and skin preparation on the part of the user. Therefore, acquiring the EEG signals in a comfortable and convenient manner is an important factor that should be incorporated into a novel BCI device. In the present study, a wearable, wireless and portable EEG-based BCI device with dry foam-based EEG sensors was developed and was demonstrated using a gaming control application. The dry EEG sensors operated without conductive gel; however, they were able to provide good conductivity and were able to acquire EEG signals effectively by adapting to irregular skin surfaces and by maintaining proper skin-sensor impedance on the forehead site. We have also demonstrated a real-time cognitive stage detection application of gaming control using the proposed portable device. The results of the present study indicate that using this portable EEG-based BCI device to conveniently and effectively control the outside world provides an approach for researching rehabilitation engineering.