nils-herrmann/clean_scirepeval huggingface dataset:
FACIAL ANIMATION AND MOTION CAPTURE : KEY ROLE FOR THE COMMUNICATION

In the last 100 years we have observed a great development of the technology. This development applied to the mass media as the radio, the cinema, the television, or Internet have influence in the human communication and the interaction of the society with the technologies. The new communicative environments need new actors (avatars) with high communicate face expression. The modeling and the animation of an expressive human face are the greatest challenges in computer graphics because we are able to detect the nuances of even the most basic movement of the face. The cinema and the video games are the fields that more have worked the creation of virtual, “real” and believable characters. With this work, we have analyzed the concrete case of the 3D animation cinema, studying how the technologies of character’s creation (with or without motion capture technique) can generate a major empathy with the user. Nowadays, the animation technique called motion capture is usually present in the production of animation cinema. Our main objectives are to evaluate if these skills can improve the facial animation and if this progress generates amplified emotional response on users.
Intentional rounding: facilitators, benefits and barriers.

To describe the implementation, practice and sustainability of Intentional Rounding (IR) within two diverse settings (aged care and maternity).



The profile of patients in hospitals has changed over time, generally being more severe, placing heavy demands on nurses' time. Routine non-urgent care is often provided only when there is time. IR has been found to increase both patient and staff satisfaction, also resulting in improved patient outcomes such as reduced falls and call bell use. IR is also used as a time management tool for safe and reliable provision of routine care.



This descriptive qualitative research study comprised of three focus groups in a metropolitan hospital.



Fifteen nurses participated in three focus groups. Seven main themes emerged from the thematic analysis of the verbatim transcripts: implementation and maintenance, how IR works, roles and responsibilities, context and environment, benefits, barriers and legal issues.



IR was quickly incorporated into normal practice, with clinicians being able to describe the main concepts and practices. IR was seen as a management tool, facilitating accountability and continuity of management support being essential for sustainability. Clinicians reported increases in patient and staff satisfaction, and the opportunity to provide patient education. While patient type and acuity, ward layout and staff experience affected the practice of IR, the principles of IR are robust enough to allow for differences in the ward specialty and patient type. However, care must be taken when implementing IR to reduce the risk of alienating experienced staff. Incorporation of IR charts into the patient health care record is recommended.


 TO CLINICAL PRACTICE
Engaging all staff, encouraging ownership and stability of management are key factors in the successful implementation and maintenance of IR. IR is flexible and robust enough to accommodate different patient types and acuity.
Arx : A DBMS with Semantically Secure Encryption Rishabh

In recent years, encrypted databases have emerged as a promising direction that provides data confidentiality without sacrificing functionality: queries are executed on encrypted data. However, existing practical proposals rely on a set of weak encryption schemes that have been shown to leak sensitive data. In this paper, we propose Arx, a practical and functionally rich database system that encrypts the data only with semantically secure encryption schemes. We show that Arx supports real applications such as ShareLaTeX and a health data cloud provider with a modest performance overhead.
SAM: Semantic Attribute Modulated Language Modeling

As a fundamental task in the natural language processing field, language modeling aims to estimate the distribution of the word sequences. However, the most existing algorithms have focused on the main texts while often ignoring the vastly-accessible semantic attributes of the documents, e.g., titles, authors, sentiments and tags. To address this issue, we propose Semantic Attribute Modulated (SAM) language modeling, a novel language modeling framework that incorporates the various semantic attributes. Attributes are selected automatically with an attribute attention mechanism. We build three text datasets with a diversity of semantic attributes. On the three text datasets, we empirically examine the language model perplexities of several typical corpora, and then demonstrate the superiority of our model with the different combinations of the attributes. Extensive qualitative results, including word semantic analysis, attention values and an interesting lyric generation, further demonstrate the effectiveness of our SAMmethod.
Conformal printing of electrically small antennas on three-dimensional surfaces.

Prof. J. T. Bernhard Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana, IL 61801, USA E-mail: jbernhar@illinois.edu Dr. E. B. Duoss ,[+,++] T. F. Malkowski ,[+] Dr. B. Y. Ahn , Prof. J. A. Lewis Department of Materials Science and Engineering University of Illinois at Urbana-Champaign Urbana, IL 61801 USA E-mail: jalewis@illinois.edu Dr. M. J. Motala , Prof. R. G. Nuzzo Department of Chemistry University of Illinois at Urbana-Champaign Urbana, Illinois 61801, USA [+] These authors contributed equally to this work.  Presently at Lawrence Livermore National Laboratory, Center for Microand NanoTechnology, Livermore, CA 94550 USA
Visualisation and Instructional Design

Human cognitive architecture includes a working memory of limited capacity and duration with partially separate visual and auditory channels, and an effectively infinite long-term memory holding many schemas that can vary in their degree of automation. These cognitive structures have evolved to handle information that varies in the extent to which elements can be processed successively in working memory or, because they interact, must be processed simultaneously imposing a heavy load on working memory. Cognitive load theory uses this combination of information and cognitive structures to guide instructional design. Several designs that rely heavily on visual working memory and its characteristics are discussed in this paper. Knowledge of human cognitive architecture is essential for instructional design, and visual cognition is a central aspect of human cognition. Not surprisingly, there are several instructional design effects that rely heavily on the manner in which humans visually process information. This paper discusses some relevant information structures, cognitive structures and instructional designs that rely on our knowledge of visual information processing. A. Information structures While considerable work by many researchers over several decades has been devoted to the organization of human cognitive architecture, far less effort has gone into investigating the information structures that must have driven the evolution of that architecture. Some work has been carried out by Sweller (1994) and Halford, Wilson and Phillips (1998). Sweller (1994) suggested that all information can be placed on a continuum according to the extent to which the elements that constitute the information interact. At one extreme, there is no interaction between the elements that need to be learned. They are independent. Element interactivity is low, or indeed, non-existent, and that means each element can be considered and learned serially without reference to any other element. Because elements at the low element interactivity end of the continuum do not interact with each other, there is no loss of understanding despite each element being learned individually and in isolation. Understanding is defined as the ability to process all elements that necessarily interact, simultaneously in working memory. Learning such material
Faces and text attract gaze independent of the task: Experimental data and computer model.

Previous studies of eye gaze have shown that when looking at images containing human faces, observers tend to rapidly focus on the facial regions. But is this true of other high-level image features as well? We here investigate the extent to which natural scenes containing faces, text elements, and cell phones-as a suitable control-attract attention by tracking the eye movements of subjects in two types of tasks-free viewing and search. We observed that subjects in free-viewing conditions look at faces and text 16.6 and 11.1 times more than similar regions normalized for size and position of the face and text. In terms of attracting gaze, text is almost as effective as faces. Furthermore, it is difficult to avoid looking at faces and text even when doing so imposes a cost. We also found that subjects took longer in making their initial saccade when they were told to avoid faces/text and their saccades landed on a non-face/non-text object. We refine a well-known bottom-up computer model of saliency-driven attention that includes conspicuity maps for color, orientation, and intensity by adding high-level semantic information (i.e., the location of faces or text) and demonstrate that this significantly improves the ability to predict eye fixations in natural images. Our enhanced model's predictions yield an area under the ROC curve over 84% for images that contain faces or text when compared against the actual fixation pattern of subjects. This suggests that the primate visual system allocates attention using such an enhanced saliency map.
Multicenter randomized clinical trial evaluating the effectiveness of the Lokomat in subacute stroke.

To compare the efficacy of robotic-assisted gait training with the Lokomat to conventional gait training in individuals with subacute stroke.



A total of 63 participants<6 months poststroke with an initial walking speed between 0.1 to 0.6 m/s completed the multicenter, randomized clinical trial. All participants received twenty-four 1-hour sessions of either Lokomat or conventional gait training. Outcome measures were evaluated prior to training, after 12 and 24 sessions, and at a 3-month follow-up exam. Self-selected overground walking speed and distance walked in 6 minutes were the primary outcome measures, whereas secondary outcome measures included balance, mobility and function, cadence and symmetry, level of disability, and quality of life measures.



Participants who received conventional gait training experienced significantly greater gains in walking speed (P=.002) and distance (P=.03) than those trained on the Lokomat. These differences were maintained at the 3-month follow-up evaluation. Secondary measures were not different between the 2 groups, although a 2-fold greater improvement in cadence was observed in the conventional versus Lokomat group.



For subacute stroke participants with moderate to severe gait impairments, the diversity of conventional gait training interventions appears to be more effective than robotic-assisted gait training for facilitating returns in walking ability.
IncludeOS: A Minimal, Resource Efficient Unikernel for Cloud Services

The emergence of cloud computing as a ubiquitous platform for elastically scaling services has generated need and opportunity for new types of operating systems. A service that needs to be both elastic and resource efficient needs A) highly specialized components, and B) to run with minimal resource overhead. Classical general purpose operating systems designed for extensive hardware support are by design far from meeting these requirements. In this paper we present IncludeOS, a single tasking library operating system for cloud services, written from scratch in C++. Key features include: extremely small disk-and memory footprint, efficient asynchronous I/O, OS-library where only what your service needs gets included, and only one device driver by default (virtio). As a test case a bootable disk image consisting of a simple DNS server with OS included is shown to require only 158 kb of disk space and to require 5-20% less CPU-time, depending on hardware, compared to the same binary running on Linux.
Simulation Modeling and Analysis of Business Process

Web services composition technology is promising technology to efficiently integrate heterogeneous, distributed and legacy software system. Performance becomes the key issue when web services composition model complex and mission-critical application. The defect of design can result in the performance bottleneck problem in the running time. In this paper, we propose a simulation-based model for the business process defined by WS-BPEL. Our model takes advantage of General Stochastic Petri Net to simulate the control flow and the transaction behavior of WS-BPEL through constructing the state transition space of composite web service. Therefore, our simulation model can accurately obtain the performance prosperities with the smaller cost in contrast to building experimental system.
Issues in Querying Databases with Design Anomalies Using Natural Language Interfaces

Accessing information is a vital activity in businesses; therefore, databases (DBs) have become necessary tools for storing their information. However, for accessing the information stored in a database, it is necessary to use a DB query language, such as SQL. Natural language interfaces to databases (NLIDBs) allow inexperienced users to obtain information from a DB using natural language expressions without the need of using a DB query language. Despite the relative effectiveness of NLIDBs, most of the approaches proposed for designing NLIDBs ignore the possibility that the DB to be queried could be poorly designed; i.e., it could have design anomalies. Unfortunately, various experiments (described in this paper) show that DB anomalies degrade the performance (recall) of NLIDBs. The purpose of this paper is to analyze the most common DB design anomalies for proposing solutions to this problem and avoid performance degradation of NLIDBs when accessing such DBs.
An Energy Efficient Compressed Sensing Framework for the Compression of Electroencephalogram Signals

The use of wireless body sensor networks is gaining popularity in monitoring and communicating information about a person's health. In such applications, the amount of data transmitted by the sensor node should be minimized. This is because the energy available in these battery powered sensors is limited. In this paper, we study the wireless transmission of electroencephalogram (EEG) signals. We propose the use of a compressed sensing (CS) framework to efficiently compress these signals at the sensor node. Our framework exploits both the temporal correlation within EEG signals and the spatial correlations amongst the EEG channels. We show that our framework is up to eight times more energy efficient than the typical wavelet compression method in terms of compression and encoding computations and wireless transmission. We also show that for a fixed compression ratio, our method achieves a better reconstruction quality than the CS-based state-of-the art method. We finally demonstrate that our method is robust to measurement noise and to packet loss and that it is applicable to a wide range of EEG signal types.
On a New Generation of Torque Controlled Light-Weight Robots

The paper describes the recent design and development efforts in DLR ́s robotics lab towards the second generation of light-weight robots. The design of the lightweight mechanics, integrated sensors and electronics is outlined. The fully sensorized joint, with motor and link position sensors as well as joint torque sensors enables the implementation of effective vibration damping and advanced control strategies for compliant manipulation. The mechatronic approach incorporates a tight collaboration between mechanics, electronics and controller design as well. Thus we hope, that important steps towards a new generation of service and personal robots have been achieved.
RF MEMS from a device perspective

This paper reviews the recent progress in MEMS for radio frequency (RF) applications from a device perspective. RF MEMS devices reviewed include switches and relays, tunable capacitors, integrated inductors, mechanical resonators and filters, and some representative microwave and millimetre-wave components. Important device parameters are highlighted, as they have significant contributions to the performance of the final products in which the devices are used. The challenges and statuses of these RF MEMS devices are outlined and discussed. The intent of this topical review is to provide perspective to newcomers in the field, and empower potential end-users with an overall device picture, current status, and a vision of their ultimate performance capabilities. (Some figures in this article are in colour only in the electronic version; see www.iop.org)
Covert channels using mobile device's magnetic field sensors

This paper presents a new covert channel using smartphone magnetic sensors. We show that modern smartphones are capable to detect the magnetic field changes induced by different computer components during I/O operations. In particular, we are able to create a covert channel between a laptop and a mobile device without any additional equipment, firmware modifications or privileged access on either of the devices. We present two encoding schemes for the covert channel communication and evaluate their effectiveness.
High-Dimensional Single-Cell Mapping of Central Nervous System Immune Cells Reveals Distinct Myeloid Subsets in Health, Aging, and Disease.

Individual reports suggest that the central nervous system (CNS) contains multiple immune cell types with diverse roles in tissue homeostasis, immune defense, and neurological diseases. It has been challenging to map leukocytes across the entire brain, and in particular in pathology, where phenotypic changes and influx of blood-derived cells prevent a clear distinction between reactive leukocyte populations. Here, we applied high-dimensional single-cell mass and fluorescence cytometry, in parallel with genetic fate mapping systems, to identify, locate, and characterize multiple distinct immune populations within the mammalian CNS. Using this approach, we revealed that microglia, several subsets of border-associated macrophages and dendritic cells coexist in the CNS at steady state and exhibit disease-specific transformations in the immune microenvironment during aging and in models of Alzheimer's disease and multiple sclerosis. Together, these data and the described framework provide a resource for the study of disease mechanisms, potential biomarkers, and therapeutic targets in CNS disease.
Structural pursuit over multiple undirected graphs.

Gaussian graphical models are useful to analyze and visualize conditional dependence relationships between interacting units. Motivated from network analysis under di erent experimental conditions, such as gene networks for disparate cancer subtypes, we model structural changes over multiple networks with possible heterogeneities. In particular, we estimate multiple precision matrices describing dependencies among interacting units through maximum penalized likelihood. Of particular interest are homogeneous groups of similar entries across and zero-entries of these matrices, referred to as clustering and sparseness structures, respectively. A non-convex method is proposed to seek a sparse representation for each matrix and identify clusters of the entries across the matrices. Computationally, we develop an e cient method on the basis of di erence convex programming, the augmented Lagrangian method and the block-wise coordinate descent method, which is scalable to hundreds of graphs of thousands nodes through a simple necessary and sufficient partition rule, which divides nodes into smaller disjoint subproblems excluding zero-coe cients nodes for arbitrary graphs with convex relaxation. Theoretically, a finite-sample error bound is derived for the proposed method to reconstruct the clustering and sparseness structures. This leads to consistent reconstruction of these two structures simultaneously, permitting the number of unknown parameters to be exponential in the sample size, and yielding the optimal performance of the oracle estimator as if the true structures were given a priori. Simulation studies suggest that the method enjoys the benefit of pursuing these two disparate kinds of structures, and compares favorably against its convex counterpart in the accuracy of structure pursuit and parameter estimation.
Web Content Analysis: Expanding the Paradigm

Are established methods of content analysis (CA) adequate to analyze web content, or should new methods be devised to address new technological developments? This chapter addresses this question by contrasting narrow and broad interpretations of the concept of web content analysis. The utility of a broad interpretation that subsumes the narrow one is then illustrated with reference to research on weblogs (blogs), a popular web format in which features of HTML documents and interactive computer-mediated communication converge. The chapter concludes by proposing an expanded Web Content Analysis (WebCA) paradigm in which insights from paradigms such as discourse analysis and social network analysis are operationalized and implemented within a general content analytic framework.
Design of a dual circular polarization microstrip patch array antenna

Design of a microstrip array antenna to achieve dual circular polarization is proposed in this paper. The proposed antenna is a 2×2 array antenna where each patch element is circularly polarized. The feed network has microstrip lines, cross slot lines and air-bridges. The array antenna can excite both right-hand circular polarization (RHCP) and left-hand circular polarization (LHCP) without using any 90° hybrid circuit or PIN diode. “Both-sided MIC Technology” is used to design the feed network as it provides flexibility to place several types of transmission lines on both sides of the dielectric substrate. The design frequency of the proposed array antenna is 10 GHz. The simulated return loss exhibits an impedance bandwidth of greater than 5% and the 3-dB axial ratio bandwidths for both RHCP and LHCP are approximately 1.39%. The structure and the basic operation along with the simulation results of the proposed dual circularly polarized array antenna are demonstrated in this paper.
A Study of Intercept Adjusted Markov Switching Vector Autoregressive Model in Economic Time Series Data

Commodity price always related to the movement of stock market index. However real economic time series data always exhibit nonlinear properties such as structural change, jumps or break in the series through time. Therefore, linear time series models are no longer suitable and Markov Switching Vector Autoregressive models which able to study the asymmetry and regime switching behavior of the data are used in the study. Intercept adjusted Markov Switching Vector Autoregressive (MSI-VAR) model is discuss and applied in the study to capture the smooth transition of the stock index changes from recession state to growth state. Results found that the dramatically changes from one state to another state are continuous smooth transition in both regimes. In addition, the 1-step prediction probability for the two regime Markov Switching model which act as the filtered probability to the actual probability of the variables is converged to the actual probability when undergo an intercept adjusted after a shift. This prove that MSI-VAR model is suitable to use in examine the changes of the economic model and able to provide significance, valid and reliable results. While oil price and gold price also proved that as a factor in affecting the stock exchange.