nils-herrmann/clean_scirepeval huggingface dataset:
Finding Optimal Bayesian Networks

In this paper, we derive optimality results for greedy Bayesian-network search algo­ rithms that perform single-edge modifica­ tions at each step and use asymptotically consistent scoring criteria. Our results ex­ tend those of Meek (1997) and Chickering (2002), who demonstrate that in the limit of large datasets, if the generative distribu­ tion is perfect with respect to a DAG defined over the observable variables, such search al­ gorithms will identify this optimal (i.e. gen­ erative) DAG model. We relax their assump­ tion about the generative distribution, and assume only that this distribution satisfies the composition property over the observable variables, which is a more realistic assump­ tion for real domains. Under this assump­ tion, we guarantee that the search algorithms identify an inclusion-optimal model; that is, a model that (1) contains the generative dis­ tribution and (2) has no sub-model that con­ tains this distribution. In addition, we show that the composition property is guaranteed to hold whenever the dependence relation­ ships in the generative distribution can be characterized by paths between singleton el­ ements in some generative graphical model (e.g. a DAG, a chain graph, or a Markov network) even when the generative model in­ cludes unobserved variables, and even when the observed data is subject to selection bias.
A service-oriented Cloud-based management system for the Internet-of-Drones

Deploying drones over the Cloud is an emerging research area motivated by the emergence of Cloud Robotics and the Internet-of-Drones (IoD) paradigms. This paper contributes to IoD and to the deployment of drones over the cloud. It presents, Dronemap Planner, an innovative service-oriented cloud based drone management system that provides access to drones through web services (SOAP and REST), schedule missions and promotes collaboration between drones. A modular cloud proxy server was developed; it acts as a moderator between drones and users. Communication between drones, users and the Dronemap Planner cloud is provided through the MAVLink protocol, which is supported by commodity drones. To demonstrate the effectiveness of Dronemap Planner, we implemented and validated it using simulated and real MAVLink-enabled drones, and deployed it on a public cloud server. Experimental results show that Dronemap Planner is efficient in virtualizing the access to drones over the Internet, and provides developers with appropriate APIs to easily program drones' applications.
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service

Machine learning methods are widely used for a variety of prediction problems. Prediction as a service is a paradigm in which service providers with technological expertise and computational resources may perform predictions for clients. However, data privacy severely restricts the applicability of such services, unless measures to keep client data private (even from the service provider) are designed. Equally important is to minimize the amount of computation and communication required between client and server. Fully homomorphic encryption offers a possible way out, whereby clients may encrypt their data, and on which the server may perform arithmetic computations. The main drawback of using fully homomorphic encryption is the amount of time required to evaluate large machine learning models on encrypted data. We combine ideas from the machine learning literature, particularly work on binarization and sparsification of neural networks, together with algorithmic tools to speed-up and parallelize computation using encrypted data.
Using fast weights to deblur old memories

Connectionist models usually have a single weight on each connection. Some interesting new properties emerge if each connection has two weights: A slowly changing, plastic weight which stores long-term knowledge and a fast-changing, elastic weight which stores temporary knowledge and spontaneously decays towards zero. If a network learns a set of associations and then these associations are "blurred" by subsequent learning, all the original associations can be "deblurred" by rehearsing on just a few of them. The rehearsal allows the fast weights to take on values that temporarily cancel out the changes in the slow weights caused by the subsequent learning.
3D seabed: 3D modeling and visualization platform for the seabed

We are building a `virtual-world' of a real world seabed for its visual analysis. Sub-bottom profile is imported in the 3D environment. “section-drilling” three-dimensional model is designed according to the characteristics of the multi-source comprehensive data under the seabed. In this model, the seabed stratigraphic profile obtained by seismic reflection is digitized into discrete points and interpolated with an approved Kriging arithmetic to produce uniform grid in every strata layer. The Delaunay triangular model is then constructed in every layer and calibrated using the drilling data to rectify the depth value of the dataset within the buffer. Finally, the constructed 3D seabed stratigraphic model is rendered in every layer by GPU shader engine. Based on this model, two state-of-the-art applications on website explorer and smartphone prove its ubiquitous feature. The resulting `3D Seabed' is used for simulation, visualization, and analysis, by a set of interlinked, real-time layers of information about the 3D Seabed and its analysis result.
A taxi business intelligence system

The increasing availability of large-scale location traces creates unprecedent opportunities to change the paradigm for knowledge discovery in transportation systems. A particularly promising area is to extract useful business intelligence, which can be used as guidance for reducing inefficiencies in energy consumption of transportation sectors, improving customer experiences, and increasing business performances. However, extracting business intelligence from location traces is not a trivial task. Conventional data analytic tools are usually not customized for handling large, complex, dynamic, and distributed nature of location traces. To that end, we develop a taxi business intelligence system to explore the massive taxi location traces from different business perspectives with various data mining functions. Since we implement the system using the real-world taxi GPS data, this demonstration will help taxi companies to improve their business performances by understanding the behaviors of both drivers and customers. In addition, several identified technical challenges also motivate data mining people to develop more sophisticate techniques in the future.
COMPACT PLANAR MICROSTRIP CROSSOVER FOR BEAMFORMING NETWORKS

The design of a fully planar microstrip crossover for beamforming networks is presented. The design starts by using a conventional half-wavelength square patch and two sets of orthogonal feeding lines. Rectangular and circular slots are introduced on the square patch in order to reduce the required area of the patch by 82%. The proposed crossover is fabricated and tested for performance confirmation. The measured data shows less than 1 dB insertion loss, more than 13 dB isolation, and around 0.1 ns deviation in the group delay across 12% fractional bandwidth. The proposed crossover is suitable for planar Butler matrix which is a key component in beamforming networks.
Seven-Level Unipolar/Bipolar Pulsed Power Generator

This paper shows how to use modular Marx multilevel converter diode (M3CD) modules to apply unipolar or bipolar high-voltage pulses for pulsed power applications. The M3CD cells allow the assembly of a multilevel converter without needing complex algorithms and parameter measurement to balance the capacitor voltages. This paper also explains how to supply all the modular cells in order to ensure galvanic isolation between control circuits and power circuits. The experimental results for a generator with seven levels, and unipolar and bipolar pulses into resistive, inductive, and capacitive loads are presented.
A new gate driver integrated circuit for IGBT devices with advanced protections

The aim of this paper is to discuss new solutions in the design of insulated gate bipolar transistor (IGBT) gate drivers with advanced protections such as two-level turn-on to reduce peak current when turning on the device, two-level turn-off to limit over-voltage when the device is turned off, and an active Miller clamp function that acts against cross conduction phenomena. Afterwards, we describe a new circuit which includes a two-level turn-off driver and an active Miller clamp function. Tests and results for these advanced functions are discussed, with particular emphasis on the influence of an intermediate level in a two-level turn-off driver on overshoot across the IGBT.
Ontology Driven Semantic Search

The introduction of semantics on the web will lead to a new generation of services based on content rather than on syntax. Search engines will provide topic-based searches, retrieving resources conceptually related to the user informational need. Queries will be expressed in several ways, and will be mapped on the semantic level defining topics that must be retrieved from the web. Moving towards this new Web era, effective semantic search engines will provide means for successful searches avoiding the heavy burden experimented by users in a classical query-string based search task. In this paper we propose a search engine based on web resource semantics. Resources to be retrieved are semantically annotated using an existing open semantic elaboration platform and an ontology is used to describe the knowledge domain into which perform queries. Ontology navigation provides semantic level reasoning in order to retrieve meaningful resources with respect to a given information request.
LBP with Six Intersection Points: Reducing Redundant Information in LBP-TOP for Micro-expression Recognition

Facial micro-expression recognition is an upcoming area in computer vision research. Up until the recent emergence of the extensive CASMEII spontaneous micro-expression database, there were numerous obstacles faced in the elicitation and labeling of data involving facial micro-expressions. In this paper, we propose the Local Binary Patterns with Six Intersection Points (LBP-SIP) volumetric descriptor based on the three intersecting lines crossing over the center point. The proposed LBP-SIP reduces the redundancy in LBP-TOP patterns, providing a more compact and lightweight representation; leading to more efficient computational complexity. Furthermore, we also incorporated a Gaussian multi-resolution pyramid to our proposed approach by concatenating the patterns across all pyramid levels. Using an SVM classifier with leave-one-sample-out cross validation, we achieve the best recognition accuracy of 67.21%, surpassing the baseline performance with further computational efficiency.
Smart smartphone development: iOS versus android

In a remarkably short timeframe, developing apps for smartphones has gone from an arcane curiosity to an essential skill set. Employers are scrambling to find developers capable of transforming their ideas into apps. Educators interested in filling that void are likewise trying to keep up, and face difficult decisions in designing a meaningful course. There are a plethora of development platforms, but two stand out because of their popularity and divergent approaches - Apple's iOS, and Google's Android. In this paper, we will compare the two, and address the question: which should faculty teach?
A pneumatic muscle hand therapy device

Intensive repetitive therapy improves function and quality of life for stroke patients. Intense therapies to overcome upper extremity impairment are beneficial, however, they are expensive because, in part, they rely on individualized interaction between the patient and rehabilitation specialist. The development of a pneumatic muscle driven hand therapy device, the Mentor/spl trade/, reinforces the need for volitional activation of joint movement while concurrently offering knowledge of results about range of motion, muscle activity or resistance to movement. The device is well tolerated and has received favorable comments from stroke survivors, their caregivers, and therapists.
Multiband Monopole Mobile Phone Antenna With Circular Polarization for GNSS Application

A novel design of a multiband monopole mobile phone antenna with circular polarization for GNSS application is presented. The proposed antenna generates four resonant frequencies with branch lines and a shorted parasitic strip to obtain a wide operating band. With the definition of 2.5:1 VSWR, the bandwidth covers several wireless communication systems, including GSM (880 ~ 960 MHz), DCS (1710 ~ 1880 MHz), PCS (1850 ~ 1990 MHz), UMTS (1920 ~ 2170 MHz), WiBro (2300 ~ 2390 MHz) and ISM (2400 ~ 2483 MHz), and also covers GNSS, including COMPASS (1559.052 ~ 1591.788 MHz), GPS (1575.42 ± 5 MHz), GLONASS (1602 ~ 1615.5 MHz). A tuning stub is added to the ground plane and the feeding strip is mounted 45 ° at the corner to achieve circular polarization for GNSS application. The 3 dB axial ratio (AR) bandwidth (AR-BW) is obtained from 1540 to 1630 MHz, covering the L1 band of GNSS, including COMPASS, GPS and GLONASS. In the 3 dB axial ratio bandwidth, right hand and left hand circularly polarizations are obtained in different broadside directions, with the peak circularly polarized gain of more than 2.7 dBic. An equivalent circuit network is used to analyze the mechanism of circular polarization. Details of the proposed antenna parameters, including return loss, radiation characteristics, and AR spectrum are presented and discussed.
A New Approach to Unsupervised Text Summarization

The paper presents a novel approach to unsupervised text summarization. The novelty lies in exploiting the diversity of concepts in text for summarization, which has not received much attention in the summarization literature. A diversity-based approach here is a principled generalization of Maximal Marginal Relevance criterion by Carbonell and Goldstein \cite{carbonell-goldstein98}.
We propose, in addition, aninformation-centricapproach to evaluation, where the quality of summaries is judged not in terms of how well they match human-created summaries but in terms of how well they represent their source documents in IR tasks such document retrieval and text categorization.
To find the effectiveness of our approach under the proposed evaluation scheme, we set out to examine how a system with the diversity functionality performs against one without, using the BMIR-J2 corpus, a test data developed by a Japanese research consortium. The results demonstrate a clear superiority of a diversity based approach to a non-diversity based approach.
An Analysis and Visualization Tool for DBLP Data

The Digital Bibliography and Library Project (DBLP) is a popular computer science bibliography website hosted at the University of Trier in Germany. It currently contains 2,722,212 computer science publications with additional information about the authors and conferences, journals, or books in which these are published. Although the database covers the majority of papers published in this field of research, it is still hard to browse the vast amount of textual data manually to find insights and correlations in it, in particular time-varying ones. This is also problematic if someone is merely interested in all papers of a specific topic and possible correlated scientific words which may hint at related papers. To close this gap, we propose an interactive tool which consists of two separate components, namely data analysis and data visualization. We show the benefits of our tool and explain how it might be used in a scenario where someone is confronted with the task of writing a state-of-the art report on a specific topic. We illustrate how data analysis, data visualization, and the human user supported by interaction features can work together to find insights which makes typical literature search tasks faster.
Measurement of Nuchal Translucency Thickness for Detection of Chromosomal Abnormalities using First Trimester Ultrasound Fetal Images

The Nuchal Translucency thickness measurement is made to identify the Down Syndrome in screening first trimester fetus and presented in this paper. The mean shift analysis and canny operators are utilized for segmenting the nuchal translucency region and the exact thickness has been estimated using Blob analysis. It is observed from the results that the fetus in the 14th week of Gestation is expected to have a nuchal translucency thickness of 1.87±0.25mm. KeywordsDown syndrome, Nuchal translucency thickness, Mean Shift Analysis and Blob analysis.
Usability metric for mobile application: a goal question metric (GQM) approach

Many usability guidelines have been created in numerous areas and mobile devices application is included as well. However, there is not much published works in relation to the usability guidelines that comes up together with metric. Although a number of measurement models (e.g. The Metric for Usability Standard in Computing [MUSiC]) have been produced for evaluating usability, they are not focusing on mobile application. This paper will make an attempt to review the existing measurement models and will further explain the development of usability metric using GQM approach. Further research will firstly develop a set of usability guidelines for mobile application which will be used to develop a metric for usability measurement.
Transition density: a new measure of activity in digital circuits

Reliability assessment is an important part of the design process of digital integrated circuits. We observe that a common thread that runs through most causes of run-time failure is the extent of circuit activity, i.e., the rate at which its nodes are switching. We propose a new measure of activity, called the transition density, which may be de ned as the \average switching rate" at a circuit node. Based on a stochastic model of logic signals, we also present an algorithm to propagate density values from the primary inputs to internal and output nodes. To illustrate the practical signi cance of this work, we demonstrate how the density values at internal nodes can be used to study circuit reliability by estimating (1) the average power & ground currents, (2) the average power dissipation, (3) the susceptibility to electromigration failures, and (4) the extent of hot-electron degradation. The density propagation algorithm has been implemented in a prototype density simulator. Using this, we present experimental results to assess the validity and feasibility of the approach. In order to obtain the same circuit activity information by traditional means, the circuit would need to be simulated for thousands of input transitions. Thus this approach is very e cient and makes possible the analysis of VLSI circuits, which are traditionally too big to simulate for long input sequences.
Real-Time Price-Based Demand Response Management for Residential Appliances via Stochastic Optimization and Robust Optimization

This paper evaluates the real-time price-based demand response (DR) management for residential appliances via stochastic optimization and robust optimization approaches. The proposed real-time price-based DR management application can be imbedded into smart meters and automatically executed on-line for determining the optimal operation of residential appliances within 5-minute time slots while considering uncertainties in real-time electricity prices. Operation tasks of residential appliances are categorized into deferrable/non-deferrable and interruptible/non-interruptible ones based on appliances' DR preferences as well as their distinct spatial and temporal operation characteristics. The stochastic optimization adopts the scenario-based approach via Monte Carlo (MC) simulation for minimizing the expected electricity payment for the entire day, while controlling the financial risks associated with real-time electricity price uncertainties via the expected downside risks formulation. Price uncertainty intervals are considered in the robust optimization for minimizing the worst-case electricity payment while flexibly adjusting the solution robustness. Both approaches are formulated as mixed-integer linear programming (MILP) problems and solved by state-of-the-art MILP solvers. The numerical results show attributes of the two approaches for solving the real-time optimal DR management problem for residential appliances.
Algorithm aversion: people erroneously avoid algorithms after seeing them err.

Research shows that evidence-based algorithms more accurately predict the future than do human forecasters. Yet when forecasters are deciding whether to use a human forecaster or a statistical algorithm, they often choose the human forecaster. This phenomenon, which we call algorithm aversion, is costly, and it is important to understand its causes. We show that people are especially averse to algorithmic forecasters after seeing them perform, even when they see them outperform a human forecaster. This is because people more quickly lose confidence in algorithmic than human forecasters after seeing them make the same mistake. In 5 studies, participants either saw an algorithm make forecasts, a human make forecasts, both, or neither. They then decided whether to tie their incentives to the future predictions of the algorithm or the human. Participants who saw the algorithm perform were less confident in it, and less likely to choose it over an inferior human forecaster. This was true even among those who saw the algorithm outperform the human.
Analysis of dynamic brain imaging data.

Modern imaging techniques for probing brain function, including functional magnetic resonance imaging, intrinsic and extrinsic contrast optical imaging, and magnetoencephalography, generate large data sets with complex content. In this paper we develop appropriate techniques for analysis and visualization of such imaging data to separate the signal from the noise and characterize the signal. The techniques developed fall into the general category of multivariate time series analysis, and in particular we extensively use the multitaper framework of spectral analysis. We develop specific protocols for the analysis of fMRI, optical imaging, and MEG data, and illustrate the techniques by applications to real data sets generated by these imaging modalities. In general, the analysis protocols involve two distinct stages: "noise" characterization and suppression, and "signal" characterization and visualization. An important general conclusion of our study is the utility of a frequency-based representation, with short, moving analysis windows to account for nonstationarity in the data. Of particular note are 1) the development of a decomposition technique (space-frequency singular value decomposition) that is shown to be a useful means of characterizing the image data, and 2) the development of an algorithm, based on multitaper methods, for the removal of approximately periodic physiological artifacts arising from cardiac and respiratory sources.