nils-herrmann/clean_scirepeval huggingface dataset:
Dynamic Facet Ordering for Faceted Product Search Engines

Faceted browsing is widely used in Web shops and product comparison sites. In these cases, a fixed ordered list of facets is often employed. This approach suffers from two main issues. First, one needs to invest a significant amount of time to devise an effective list. Second, with a fixed list of facets, it can happen that a facet becomes useless if all products that match the query are associated to that particular facet. In this work, we present a framework for dynamic facet ordering in e-commerce. Based on measures for specificity and dispersion of facet values, the fully automated algorithm ranks those properties and facets on top that lead to a quick drill-down for any possible target product. In contrast to existing solutions, the framework addresses e-commerce specific aspects, such as the possibility of multiple clicks, the grouping of facets by their corresponding properties, and the abundance of numeric facets. In a large-scale simulation and user study, our approach was, in general, favorably compared to a facet list created by domain experts, a greedy approach as baseline, and a state-of-the-art entropy-based solution.
Intimate Partner Violence as a Risk Factor for Mental Disorders: A Meta-Analysis

This article reviews literature on the prevalence of mental health problems among women with a history of intimate partner violence. The weighted mean prevalence of mental health problems among battered women was 47.6% in 18 studies of depression, 17.9% in 13 studies of suicidality, 63.8% in 11 studies of posttraumatic stress disorder (PTSD), 18.5% in 10 studies of alcohol abuse, and 8.9% in four studies of drug abuse. These were typically inconsistent across studies. Weighted mean odds ratios representing associations of these problems with violence ranged from 3.55 to 5.62, and were typically consistent across studies. Variability was accounted for by differences in sampling frames. Dose-response relationships of violence to depression and PTSD were observed. Although research has not addressed many criteria for causal inferences, the existing research is consistent with the hypothesis that intimate partner violence increases risk for mental health problems. The appropriate way to conceptualize these problems deserves careful attention.
Design, fabrication and analysis of a body-caudal fin propulsion system for a microrobotic fish

In this paper, we present the design and fabrication of a centimeter-scale propulsion system for a robotic fish. The key to the design is selection of an appropriate actuator and a body frame that is simple and compact. SMA spring actuators are customized to provide the necessary work output for the microrobotic fish. The flexure joints, electrical wiring and attachment pads for SMA actuators are all embedded in a single layer of copper laminated polymer film, sandwiched between two layers of glass fiber. Instead of using individual actuators to rotate each joint, each actuator rotates all the joints to a certain mode shape and undulatory motion is created by a timed sequence of these mode shapes. Subcarangiform swimming mode of minnows has been emulated using five links and four actuators. The size of the four-joint propulsion system is 6 mm wide, 40 mm long with the body frame thickness of 0.25 mm.
Robust and Collective Entity Disambiguation through Semantic Embeddings

Entity disambiguation is the task of mapping ambiguous terms in natural-language text to its entities in a knowledge base. It finds its application in the extraction of structured data in RDF (Resource Description Framework) from textual documents, but equally so in facilitating artificial intelligence applications, such as Semantic Search, Reasoning and Question & Answering. We propose a new collective, graph-based disambiguation algorithm utilizing semantic entity and document embeddings for robust entity disambiguation. Robust thereby refers to the property of achieving better than state-of-the-art results over a wide range of very different data sets. Our approach is also able to abstain if no appropriate entity can be found for a specific surface form. Our evaluation shows, that our approach achieves significantly (>5%) better results than all other publicly available disambiguation algorithms on 7 of 9 datasets without data set specific tuning. Moreover, we discuss the influence of the quality of the knowledge base on the disambiguation accuracy and indicate that our algorithm achieves better results than non-publicly available state-of-the-art algorithms.
Understanding and executing instructions for everyday manipulation tasks from the World Wide Web

Service robots will have to accomplish more and more complex, open-ended tasks and regularly acquire new skills. In this work, we propose a new approach to the problem of generating plans for such household robots. Instead composing them from atomic actions — the common approach in robot planning — we propose to transform task descriptions on web sites like ehow.com into executable robot plans. We present methods for automatically converting the instructions from natural language into a formal, logic-based representation, for resolving the word senses using the WordNet database and the Cyc ontology, and for exporting the generated plans into the mobile robot's plan language RPL. We discuss the problem of inferring information that is missing in these descriptions and the problem of grounding the abstract task descriptions in the perception and action system, and we propose techniques for solving them. The whole system works autonomously without human interaction. It has successfully been tested with a set of about 150 natural language directives, of which up to 80% could be correctly transformed.
Design of Neural Network PID Controller Based on Brushless DC Motor

For the performance and accuracy requirements of brushless DC motor speed control system, this paper integrates organically neural network and the traditional PID to constitute brushless DC motor speed control system based on BP neural network self-tuning parameters PID control. The traditional PID controller is used in the beginning several seconds, and then another parameter self-tuning PID controller based on BP neural network is converted to after training for seconds. Simulation model was established in Matlab/Simulink. The simulation results indicate that the neutral network PID controller can improve the robustness of the system and has better adaptabilities to the model and environments compared with the traditional PID controller.
Temporal Analytics on Big Data for Web Advertising

"Big Data" in map-reduce (M-R) clusters is often fundamentally temporal in nature, as are many analytics tasks over such data. For instance, display advertising uses Behavioral Targeting (BT) to select ads for users based on prior searches, page views, etc. Previous work on BT has focused on techniques that scale well for offline data using M-R. However, this approach has limitations for BT-style applications that deal with temporal data: (1) many queries are temporal and not easily expressible in M-R, and moreover, the set-oriented nature of M-R front-ends such as SCOPE is not suitable for temporal processing, (2) as commercial systems mature, they may need to also directly analyze and react to real-time data feeds since a high turnaround time can result in missed opportunities, but it is difficult for current solutions to naturally also operate over real-time streams. Our contributions are twofold. First, we propose a novel framework called TiMR (pronounced timer), that combines a time-oriented data processing system with a M-R framework. Users write and submit analysis algorithms as temporal queries - these queries are succinct, scale-out-agnostic, and easy to write. They scale well on large-scale offline data using TiMR, and can work unmodified over real-time streams. We also propose new cost-based query fragmentation and temporal partitioning schemes for improving efficiency with TiMR. Second, we show the feasibility of this approach for BT, with new temporal algorithms that exploit new targeting opportunities. Experiments using real data from a commercial ad platform show that TiMR is very efficient and incurs orders-of-magnitude lower development effort. Our BT solution is easy and succinct, and performs up to several times better than current schemes in terms of memory, learning time, and click-through-rate/coverage.
A Generative Process for Contractive Auto-Encoders

The contractive auto-encoder learns a representation of the input data that captures the local manifold structure around each data point, through the leading singular vectors of the Jacobian of the transformation from input to representation. The corresponding singular values specify how much local variation is plausible in directions associated with the corresponding singular vectors, while remaining in a high-density region of the input space. This paper proposes a procedure for generating samples that are consistent with the local structure captured by a contractive auto-encoder. The associated stochastic process defines a distribution from which one can sample, and which experimentally appears to converge quickly and mix well between modes, compared to Restricted Boltzmann Machines and Deep Belief Networks. The intuitions behind this procedure can also be used to train the second layer of contraction that pools lower-level features and learns to be invariant to the local directions of variation discovered in the first layer. We show that this can help learn and represent invariances present in the data and improve classification error.
Recent Advances and Industrial Applications of Multilevel Converters

Multilevel converters have been under research and development for more than three decades and have found successful industrial application. However, this is still a technology under development, and many new contributions and new commercial topologies have been reported in the last few years. The aim of this paper is to group and review these recent contributions, in order to establish the current state of the art and trends of the technology, to provide readers with a comprehensive and insightful review of where multilevel converter technology stands and is heading. This paper first presents a brief overview of well-established multilevel converters strongly oriented to their current state in industrial applications to then center the discussion on the new converters that have made their way into the industry. In addition, new promising topologies are discussed. Recent advances made in modulation and control of multilevel converters are also addressed. A great part of this paper is devoted to show nontraditional applications powered by multilevel converters and how multilevel converters are becoming an enabling technology in many industrial sectors. Finally, some future trends and challenges in the further development of this technology are discussed to motivate future contributions that address open problems and explore new possibilities.
Uses and Gratifications of digital photo sharing on Facebook

Despite the rapid adoption of Facebook as a means of photo sharing, minimal research has been conducted to understand user gratification behind this activity. In order to address this gap, the current study examines users’ gratifications in sharing photos on Facebook by applying Uses and Gratification (U&G) theory. An online survey completed by 368 respondents identified six different gratifications, namely, affection, attention seeking, disclosure, habit, information sharing, and social influence, behind sharing digital photos on Facebook. Some of the study’s prominent findings were: age was in positive correlation with disclosure and social influence gratifications; gender differences were identified among habit and disclosure gratifications; number of photos shared was negatively correlated with habit and information sharing gratifications. The study’s implications can be utilized to refine existing and develop new features and services bridging digital photos and social networking services.
Mining User Check-In Behavior with a Random Walk for Urban Point-of-Interest Recommendations

In recent years, research into the mining of user check-in behavior for point-of-interest (POI) recommendations has attracted a lot of attention. Existing studies on this topic mainly treat such recommendations in a traditional manner—that is, they treat POIs as items and check-ins as ratings. However, users usually visit a place for reasons other than to simply say that they have visited. In this article, we propose an approach referred to as Urban POI-Walk (UPOI-Walk), which takes into account a user's social-triggered intentions (SI), preference-triggered intentions (PreI), and popularity-triggered intentions (PopI), to estimate the probability of a user checking-in to a POI. The core idea of UPOI-Walk involves building a HITS-based random walk on the normalized check-in network, thus supporting the prediction of POI properties related to each user's preferences. To achieve this goal, we define several user--POI graphs to capture the key properties of the check-in behavior motivated by user intentions. In our UPOI-Walk approach, we propose a new kind of random walk model—Dynamic HITS-based Random Walk—which comprehensively considers the relevance between POIs and users from different aspects. On the basis of similitude, we make an online recommendation as to the POI the user intends to visit. To the best of our knowledge, this is the first work on urban POI recommendations that considers user check-in behavior motivated by SI, PreI, and PopI in location-based social network data. Through comprehensive experimental evaluations on two real datasets, the proposed UPOI-Walk is shown to deliver excellent performance.
Seamless kernel updates

Kernel patches are released frequently to fix bugs and security vulnerabilities. However, users and system administrators often delay installing these updates because they require a system reboot, which results in disruption of service and the loss of application state. Unfortunately, the longer a system remains out-of-date, the higher is the likelihood of system failure or a successful attack. Approaches, such as dynamic patching and hot swapping, have been proposed for updating the kernel. All of them either limit the types of updates that are supported, or require significant programming effort to manage. We have designed a system that checkpoints application-visible state, updates the kernel, and restores the application state thus minimizing disruption of service. By checkpointing high-level state, our system no longer depends on the precise implementation of a patch and can apply all backward compatible patches. Our results show that updates to major releases of the Linux kernel can be applied with minimal effort and no observable overhead.
FPGA Implementation of an Embedded Robust Adaptive Controller for Autonomous Omnidirectional Mobile Platform

This paper presents an embedded adaptive robust controller for trajectory tracking and stabilization of an omnidirectional mobile platform with parameter variations and uncertainties caused by friction and slip. Based on a dynamic model of the platform, the adaptive controller to achieve point stabilization, trajectory tracking, and path following is synthesized via the adaptive backstepping approach. This robust adaptive controller is then implemented into a high-performance field-programmable gate array chip using hardware/software codesign technique and system-on-a-programmable-chip design concept with a reusable user intellectual property core library. Furthermore, a soft-core processor and a real-time operating system are embedded into the same chip for realizing the control law to steer the mobile platform. Simulation results are conducted to show the effectiveness and merit of the proposed control method in comparison with a conventional proportional-integral feedback controller. The performance and applicability of the proposed embedded adaptive controller are exemplified by conducting several experiments on an autonomous omnidirectional mobile robot.
A supply chain network equilibrium model with random demands

In this paper, we develop a supply chain network model consisting of manufacturers and retailers in which the demands associated with the retail outlets are random. We model the optimizing behavior of the various decision-makers, derive the equilibrium conditions, and establish the finite-dimensional variational inequality formulation. We provide qualitative properties of the equilibrium pattern in terms of existence and uniqueness results and also establish conditions under which the proposed computational procedure is guaranteed to converge. Finally, we illustrate the model through several numerical examples for which the equilibrium prices and product shipments are computed. This is the first supply chain network equilibrium model with random demands for which modeling, qualitative analysis, and computational results have been obtained.
DESIGN OF A 2-FINGER HAND EXOSKELETON FOR VR GRASPING SIMULATION

There are numerous applications of VR simulation requiring the grasping and manipulation of virtual objects. Standard-use haptic interfaces (e.g. PHANToM) allow only a limited level of realism, as grasping is approximated through a metaphor (e.g. pressing a button for grasping an object). Existing hand exoskeletons have also certain drawbacks (e.g. feedback only for finger flexion, limited finger workspace etc.). The work presented in this paper introduces a hand force exoskeleton that allows full finger flexion and extension and applies bi-directional feedback. It offers 3dof for the index finger and 4 for the thumb. The system is actuated by DC motors and a cable transmission is used. It has been designed for use in conjunction with a commercial 6dof haptic arm in order to allow the simulation of external forces.
Deadline Based Resource Provisioningand Scheduling Algorithm for Scientific Workflows on Clouds

Cloud computing is the latest distributed computing paradigm and it offers tremendous opportunities to solve large-scale scientific problems. However, it presents various challenges that need to be addressed in order to be efficiently utilized for workflow applications. Although the workflow scheduling problem has been widely studied, there are very few initiatives tailored for cloud environments. Furthermore, the existing works fail to either meet the user's quality of service (QoS) requirements or to incorporate some basic principles of cloud computing such as the elasticity and heterogeneity of the computing resources. This paper proposes a resource provisioning and scheduling strategy for scientific workflows on Infrastructure as a Service (IaaS) clouds. We present an algorithm based on the meta-heuristic optimization technique, particle swarm optimization (PSO), which aims to minimize the overall workflow execution cost while meeting deadline constraints. Our heuristic is evaluated using CloudSim and various well-known scientific workflows of different sizes. The results show that our approach performs better than the current state-of-the-art algorithms.
PubChem: a public information system for analyzing bioactivities of small molecules

PubChem (http://pubchem.ncbi.nlm.nih.gov) is a public repository for biological properties of small molecules hosted by the US National Institutes of Health (NIH). PubChem BioAssay database currently contains biological test results for more than 700 000 compounds. The goal of PubChem is to make this information easily accessible to biomedical researchers. In this work, we present a set of web servers to facilitate and optimize the utility of biological activity information within PubChem. These web-based services provide tools for rapid data retrieval, integration and comparison of biological screening results, exploratory structure-activity analysis, and target selectivity examination. This article reviews these bioactivity analysis tools and discusses their uses. Most of the tools described in this work can be directly accessed at http://pubchem.ncbi.nlm.nih.gov/assay/. URLs for accessing other tools described in this work are specified individually.
Collaborative filtering with privacy via factor analysis

Collaborative filtering (CF) is valuable in e-commerce, and for direct recommendations for music, movies, news etc. But today's systems have several disadvantages, including privacy risks. As we move toward ubiquitous computing, there is a great potential for individuals to share all kinds of information about places and things to do, see and buy, but the privacy risks are severe. In this paper we describe a new method for collaborative filtering which protects the privacy of individual data. The method is based on a probabilistic factor analysis model. Privacy protection is provided by a peer-to-peer protocol which is described elsewhere, but outlined in this paper. The factor analysis approach handles missing data without requiring default values for them. We give several experiments that suggest that this is most accurate method for CF to date. The new algorithm has other advantages in speed and storage over previous algorithms. Finally, we suggest applications of the approach to other kinds of statistical analyses of survey or questionaire data.
Large-scale assessment of mobile notifications

Notifications are a core feature of mobile phones. They inform users about a variety of events. Users may take immediate action or ignore them depending on the importance of a notification as well as their current context. The nature of notifications is manifold, applications use them both sparsely and frequently. In this paper we present the first large-scale analysis of mobile notifications with a focus on users' subjective perceptions. We derive a holistic picture of notifications on mobile phones by collecting close to 200 million notifications from more than 40,000 users. Using a data-driven approach, we break down what users like and dislike about notifications. Our results reveal differences in importance of notifications and how users value notifications from messaging apps as well as notifications that include information about people and events. Based on these results we derive a number of findings about the nature of notifications and guidelines to effectively use them.
SecVisor: a tiny hypervisor to provide lifetime kernel code integrity for commodity OSes

We propose SecVisor, a tiny hypervisor that ensures code integrity for commodity OS kernels. In particular, SecVisor ensures that only user-approved code can execute in kernel mode over the entire system lifetime. This protects the kernel against code injection attacks, such as kernel rootkits. SecVisor can achieve this propertyeven against an attacker who controls everything but the CPU, the memory controller, and system memory chips. Further, SecVisor can even defend against attackers with knowledge of zero-day kernel exploits.
 Our goal is to make SecVisor amenable to formal verificationand manual audit, thereby making it possible to rule out known classes of vulnerabilities. To this end, SecVisor offers small code size and small external interface. We rely on memory virtualization to build SecVisor and implement two versions, one using software memory virtualization and the other using CPU-supported memory virtualization. The code sizes of the runtime portions of these versions are 1739 and 1112 lines, respectively. The size of the external interface for both versions of SecVisor is 2 hypercalls. It is easy to port OS kernels to SecVisor. We port the Linux kernel version 2.6.20 by adding 12 lines and deleting 81 lines, out of a total of approximately 4.3 million lines of code in the kernel.