nils-herrmann/clean_scirepeval huggingface dataset:
3D Collaboration Method over HoloLens™ and Skype™ End Points

This paper describes a new Augmented Reality (AR) system called HoloLens developed by Microsoft, and the interaction model for supporting collaboration in this space with other users. Whereas traditional AR collaboration is between two or more head-mounted displays (HMD) users, we describe collaboration between a single HMD user and others who join the space by hitching on the view of the HMD user. The remote companions participate remotely through Skype-enabled devices such as tablets or PC's. The interaction is novel in the use of a 3D space with digital objects where the interaction by remote parties can be achieved asynchronously and reflected back to the primary user. We describe additional collaboration scenarios possible with this arrangement.
The evidence for 'flipping out': A systematic review of the flipped classroom in nursing education.

The flipped classroom has generated interest in higher education providing a student-centred approach to learning. This has the potential to engage nursing students in ways that address the needs of today's students and the complexity of contemporary healthcare. Calls for educational reform, particularly in healthcare programs such as nursing, highlight the need for students to problem-solve, reason and apply theory into practice. The drivers towards student-based learning have manifested in team, problem and case-based learning models. Though there has been a shift towards the flipped classroom, comparatively little is known about how it is used in nursing curricula.



The aims of this systematic review were to examine how the flipped classroom has been applied in nursing education and outcomes associated with this style of teaching.



Five databases were searched and resulted in the retrieval of 21 papers: PubMed, CINAHL, EMBASE, Scopus and ERIC.



After screening for inclusion/exclusion criteria, each paper was evaluated using a critical appraisal tool. Data extraction and analysis were completed on all included studies.



This systematic review screened 21 titles and abstracts resulting in nine included studies. All authors critically appraised the quality of the included studies. Five studies were identified and themes identified were: academic performance outcomes, and student satisfaction implementing the flipped classroom.



Use of the flipped classroom in higher education nursing programmes yielded neutral or positive academic outcomes and mixed results for satisfaction. Engagement of students in the flipped classroom model was achieved when academics informed and rationalised the purpose of the flipped classroom model to students. However, no studies in this review identified the evaluation of the process of implementing the flipped classroom. Studies examining the process and ongoing evaluation and refinement of the flipped classroom in higher education nursing programmes are warranted.
Smart Parking Guidance, Monitoring and Reservations: A Review

As the urban population is increasing, more and more cars are circulating in the city to search for parking spaces which contributes to the global problem of traffic congestion. To alleviate the parking problems, smart parking systems must be implemented. In this paper, the background on parking problems is introduced and relevant algorithms, systems, and techniques behind the smart parking are reviewed and discussed. This paper provides a good insight into the guidance, monitoring and reservations components of the smart car parking and directions to the future development.
LC-QuAD: A Corpus for Complex Question Answering over Knowledge Graphs

Being able to access knowledge bases in an intuitive way has been an active area of research over the past years. In particular, several question answering (QA) approaches which allow to query RDF datasets in natural language have been developed as they allow end users to access knowledge without needing to learn the schema of a knowledge base and learn a formal query language. To foster this research area, several training datasets have been created, e.g. in the QALD (Question Answering over Linked Data) initiative. However, existing datasets are insufficient in terms of size, variety or complexity to apply and evaluate a range of machine learning based QA approaches for learning complex SPARQL queries. With the provision of the Large-Scale Complex Question Answering Dataset (LC-QuAD), we close this gap by providing a dataset with 5000 questions and their corresponding SPARQL queries over the DBpedia dataset. In this article, we describe the dataset creation process and how we ensure a high variety of questions, which should enable to assess the robustness and accuracy of the next generation of QA systems for knowledge graphs.
Pole-zero analysis of multi-stage amplifiers: A tutorial overview

Analyzing pole-zero locations of an amplifier is essential to 1) understand the characteristics of a circuit in the frequency domain, and 2) choose appropriate frequency compensation techniques to guarantee the stability of a circuit over a specified range of load resistance and capacitance. The objective of this paper is to provide tutorial treatment of the steps for analyzing poles and zeros in multi-stage amplifiers. These techniques can be equally applied for the analysis of power management circuits such as low-dropout voltage regulators (LDOs) and controllers for DC-DC converters.
A critical consideration of social networking sites ’ addiction potential

The evolution of the Internet and its applications has led to a notable increase in concern about social networking sites (SNSs). SNSs have had global mass appeal and their often frequent use – usually by young people – has triggered worries, discussions and studies on the topic of technological and social networking addictions. In addressing this issue, we have to ask to what extent technological and social networking addictions are of the same nature as substance addictions, and whether the consequences they lead to, if any, are severe enough to merit clinical attention. We can summarize our position on the topic by saying that SNSs are primarily used to increase social capital and that there is not currently enough empirical evidence on SNSs’ addiction potential to claim that SNS addition exists. Although SNSs can provoke certain negative consequences in a subset of their users or provide a platform for the expression of preexisting conditions, this is not sufficient support for their standalone addictive power. It is necessary to distinguish between true addictive disorders, the kind that fall under the category of substance addictions, and the negative side-effects of engaging with certain appealing activities like SNSs so that we do not undermine the severity of psychiatric disorders and the experience of the individuals suffering from them. We propose that psychoeducation, viewing SNS use in context to understand their gratifications and compensatory functions and revisiting the terminology on the subject are sufficient to address the problems that emerge from SNS usage. ARTICLE HISTORY Received 17 June 2015 Revised 1 June 2016 Accepted 1 June 2016 Published online 4 July 2016
Analysis, Synthesis, and Retargeting of Facial Expressions

Computer animated characters have recently gained popularity in many applications, including web pages, computer games, movies, and various human computer interface designs. In order to make these animated characters lively and convincing, they require sophisticated facial expressions and motions. Traditionally, these animations are produced entirely by skilled artists. Although the quality of manually produced animation remains the best, this process is slow and costly. Motion capture performance of actors and actresses is one technique that attempts to speed up this process. One problem with this technique is that the captured motion data can not be edited easily. In recent years, statistical techniques have been used to address this problem by learning the mapping between audio speech and facial motion. New facial motion can be synthesized for novel audio data by reusing the motion capture data. However, since facial expressions are not modeled in these approaches, the resulting facial animation is realistic, yet expressionless. This thesis takes an expressionless talking face and creates an expressive facial animation. This process consists of three parts: expression synthesis, blendshape retargeting, and head motion synthesis. Expression synthesis uses a factorization model to describe the interaction between facial expression and speech content underlying each particular facial appearance. A new facial expression can be applied to novel input video, while retaining the same speech content. Blendshape retargeting maps facial expressions onto a 3D face model using the framework of blendshape interpolation. Three methods of sampling the keyshapes, or the prototype shapes, from data are evaluated. In addition, the generality of blendshape retargeting is demonstrated in three different domains. Head motion synthesis uses audio pitch contours to derive
A Multi-robot Control Policy for Information Gathering in the Presence of Unknown Hazards

This paper addresses the problem of deploying a network of robots into an environment, where the environment is hazardous to the robots. This may mean that there are adversarial agents in the environment trying to disable the robots, or that some regions of the environment tend to make the robots fail, for example due to radiation, fire, adverse weather, or caustic chemicals. A probabilistic model of the environment is formulated, under which recursive Bayesian filters are used to estimate the environment events and hazards online. The robots must control their positions both to avoid sensor failures and to provide useful sensor information by following the analytical gradient of mutual information computed using these online estimates. Mutual information is shown to combine the competing incentives of avoiding failure and collecting informative measurements under a common objective. Simulations demonstrate the performance of the algorithm. Mac Schwager GRASP Lab, University of Pennsylvania, 3330 Walnut St, PA 19104, USA, and Computer Science and Artificial Intelligence Lab, MIT, 32 Vassar St, Cambridge, MA 02139, USA e-mail: schwager@seas.upenn.edu Philip Dames GRASP Lab, University of Pennsylvania, 3330 Walnut St, PA 19104, USA e-mail: pdames@seas.upenn.edu Daniela Rus Computer Science and Artificial Intelligence Lab, MIT, 32 Vassar St, Cambridge, MA 02139, USA e-mail: rus@csail.mit.edu Vijay Kumar GRASP Lab, University of Pennsylvania, 3330 Walnut St, PA 19104, USA e-mail: kumar@seas.upenn.edu
A new DNN-based high quality pronunciation evaluation for computer-aided language learning (CALL)

In this paper, we propose to use Deep Neural Net (DNN), which has been recently shown to reduce speech recognition errors significantly, in Computer-Aided Language Learning (CALL) to evaluate English learners’ pronunciations. Multi-layer, stacked Restricted Boltzman Machines (RBMs), are first trained as nonlinear basis functions to represent speech signals succinctly, and the output layer is discriminatively trained to optimize the posterior probabilities of correct, sub-phonemic “senone” states. Three Goodness of Pronunciation (GOP) scores, including: the likelihood-based posterior probability, averaged framelevel posteriors of the DNN output layer “senone” nodes, and log likelihood ratio of correct and competing models, are tested with recordings of both native and non-native speakers, along with manual grading of pronunciation quality. The experimental results show that the GOP estimated by averaged frame-level posteriors of “senones” correlate with human scores the best. Comparing with GOPs estimated with non-DNN, i.e. GMMHMM, based models, the new approach can improve the correlations relatively by 22.0% or 15.6%, at word or sentence levels, respectively. In addition, the frame-level posteriors, which doesn’t need a decoding lattice and its corresponding forwardbackward computations, is suitable for supporting fast, on-line, multi-channel applications.
140-GHz ${\rm TE}_{20}$-Mode Dielectric-Loaded SIW Slot Antenna Array in LTCC

A <formula formulatype="inline"><tex Notation="TeX">${\rm TE}_{20}$</tex> </formula>-mode substrate integrated waveguide (SIW) fed slot antenna array is proposed at 140 GHz for fabrication ease and cost reduction. The antenna array consists of a feeding network, radiators, and dielectric loadings. The proposed antenna array and an SIW-waveguide transition are integrated into the multilayered low-temperature co-fired ceramic (LTCC). The feeding network comprises a power divider and <formula formulatype="inline"><tex Notation="TeX">$E$</tex> </formula>-plane couplers that support the <formula formulatype="inline"> <tex Notation="TeX">${\rm TE}_{20}$</tex></formula> mode. Each pair of symmetrical longitudinal radiating slots is fed by the <formula formulatype="inline"> <tex Notation="TeX">${\rm TE}_{20}$</tex></formula> mode in a doubled-width SIW. The pair of radiating slots is loaded by a doubled-width open-ended via-fence structure in which a higher order mode is also excited. Experimental results show that an 8 <formula formulatype="inline"><tex Notation="TeX">$\times$</tex> </formula> 8 slot array with the transition achieves a maximum boresight gain of 21.3 dBi at 140.6 GHz and an impedance matching bandwidth of 129.2–146 GHz with a boresight gain above 18.3 dBi.
From Wearable Sensors to Smart Implants-–Toward Pervasive and Personalized Healthcare

This paper discusses the evolution of pervasive healthcare from its inception for activity recognition using wearable sensors to the future of sensing implant deployment and data processing.  We provide an overview of some of the past milestones and recent developments, categorized into different generations of pervasive sensing applications for health monitoring. This is followed by a review on recent technological advances that have allowed unobtrusive continuous sensing combined with diverse technologies to reshape the clinical workflow for both acute and chronic disease management. We discuss the opportunities of pervasive health monitoring through data linkages with other health informatics systems including the mining of health records, clinical trial databases, multiomics data integration, and social media.  Technical advances have supported the evolution of the pervasive health paradigm toward preventative, predictive, personalized, and participatory medicine.  The sensing technologies discussed in this paper and their future evolution will play a key role in realizing the goal of sustainable healthcare systems.
Kinematics of a 3-DOF parallel manipulator with an R-P-S joint structure

A new solution of the inverse kinematics task for a 3-DOF parallel manipulator with a R-P -S joint structure is obtained for a given position of end-effector in the form of simple position equations. Based on this the number of the inverse kinematics task solutions was investigated, in general, equal to four. We identify the size of the manipulator feasible area and simple relationships are found between the position and orientation of the platform. We prove a new theorem stating that, while the end-effector traces a circular horizontal path with its centre at the vertical z-axis, the norm of the joint coordinates vector remains constant.
Hole filling method using depth based in-painting for view synthesis in free viewpoint television and 3-D video

Depth image-based rendering (DIBR) is generally used to synthesize virtual view images in free viewpoint television (FTV) and three-dimensional (3-D) video. One of the main problems in DIBR is how to fill the holes caused by disocclusion regions and inaccurate depth values. In this paper, we propose a new hole filling method using a depth based in-painting technique. Experimental results show that the proposed hole filling method provides improved rendering quality both objectively and subjectively.
A Wireless Flexible Sensorized Insole for Gait Analysis

This paper introduces the design and development of a novel pressure-sensitive foot insole for real-time monitoring of plantar pressure distribution during walking. The device consists of a flexible insole with 64 pressure-sensitive elements and an integrated electronic board for high-frequency data acquisition, pre-filtering, and wireless transmission to a remote data computing/storing unit. The pressure-sensitive technology is based on an optoelectronic technology developed at Scuola Superiore Sant'Anna. The insole is a low-cost and low-power battery-powered device. The design and development of the device is presented along with its experimental characterization and validation with healthy subjects performing a task of walking at different speeds, and benchmarked against an instrumented force platform.
Survey in Smart Grid and Smart Home Security: Issues, Challenges and Countermeasures

The electricity industry is now at the verge of a new era-an era that promises, through the evolution of the existing electrical grids to smart grids, more efficient and effective power management, better reliability, reduced production costs, and more environmentally friendly energy generation. Numerous initiatives across the globe, led by both industry and academia, reflect the mounting interest around not only the enormous benefits but also the great risks introduced by this evolution. This paper focuses on issues related to the security of the smart grid and the smart home, which we present as an integral part of the smart grid. Based on several scenarios, we aim to present some of the most representative threats to the smart home/smart grid environment. The threats detected are categorized according to specific security goals set for the smart home/smart grid environment, and their impact on the overall system security is evaluated. A review of contemporary literature is then conducted with the aim of presenting promising security countermeasures with respect to the identified specific security goals for each presented scenario. An effort to shed light on open issues and future research directions concludes this paper.
Detection of opinion spam based on anomalous rating deviation

The publication of fake reviews by parties with vested interests has become a severe problem for consumers who use online product reviews in their decision making. To counter this problem a number of methods for detecting these fake reviews, termed opinion spam, have been proposed. However, to date, many of these methods focus on analysis of review text, making them unsuitable for many review systems where accompanying text is optional, or not possible. Moreover, these approaches are often computationally expensive, requiring extensive resources to handle text analysis over the scale of data typically involved. In this paper, we consider opinion spammers manipulation of average ratings for products, focusing on differences between spammer ratings and the majority opinion of honest reviewers. We propose a lightweight, effective method for detecting opinion spammers based on these differences. This method uses binomial regression to identify reviewers having an anomalous proportion of ratings that deviate from the majority opinion. Experiments on real-world and synthetic data show that our approach is able to successfully identify opinion spammers. Comparison with the current state-of-the-art approach, also based only on ratings, shows that our method is able to achieve similar detection accuracy while removing the need for assumptions regarding probabilities of spam and non-spam reviews and reducing the heavy computation required for learning.
To offload or not to offload? The bandwidth and energy costs of mobile cloud computing

The cloud seems to be an excellent companion of mobile systems, to alleviate battery consumption on smartphones and to backup user's data on-the-fly. Indeed, many recent works focus on frameworks that enable mobile computation offloading to software clones of smartphones on the cloud and on designing cloud-based backup systems for the data stored in our devices. Both mobile computation offloading and data backup involve communication between the real devices and the cloud. This communication does certainly not come for free. It costs in terms of bandwidth (the traffic overhead to communicate with the cloud) and in terms of energy (computation and use of network interfaces on the device). In this work we study the fmobile software/data backupseasibility of both mobile computation offloading and mobile software/data backups in real-life scenarios. In our study we assume an architecture where each real device is associated to a software clone on the cloud. We consider two types of clones: The off-clone, whose purpose is to support computation offloading, and the back-clone, which comes to use when a restore of user's data and apps is needed. We give a precise evaluation of the feasibility and costs of both off-clones and back-clones in terms of bandwidth and energy consumption on the real device. We achieve this through measurements done on a real testbed of 11 Android smartphones and an equal number of software clones running on the Amazon EC2 public cloud. The smartphones have been used as the primary mobile by the participants for the whole experiment duration.
A silicon-based nuclear spin quantum computer

Quantum computers promise to exceed the computational efficiency of ordinary classical machines because quantum algorithms allow the execution of certain tasks in fewer steps. But practical implementation of these machines poses a formidable challenge. Here I present a scheme for implementing a quantum-mechanical computer. Information is encoded onto the nuclear spins of donor atoms in doped silicon electronic devices. Logical operations on individual spins are performed using externally applied electric fields, and spin measurements are made using currents of spin-polarized electrons. The realization of such a computer is dependent on future refinements of conventional silicon electronics.
VirtualHome: Simulating Household Activities Via Programs

In this paper, we are interested in modeling complex activities that occur in a typical household. We propose to use programs, i.e., sequences of atomic actions and interactions, as a high level representation of complex tasks. Programs are interesting because they provide a non-ambiguous representation of a task, and allow agents to execute them. However, nowadays, there is no database providing this type of information. Towards this goal, we first crowd-source programs for a variety of activities that happen in people's homes, via a game-like interface used for teaching kids how to code. Using the collected dataset, we show how we can learn to extract programs directly from natural language descriptions or from videos. We then implement the most common atomic (inter)actions in the Unity3D game engine, and use our programs to "drive" an artificial agent to execute tasks in a simulated household environment. Our VirtualHome simulator allows us to create a large activity video dataset with rich ground-truth, enabling training and testing of video understanding models. We further showcase examples of our agent performing tasks in our VirtualHome based on language descriptions.