nils-herrmann/clean_scirepeval huggingface dataset:
Does a Water Protocol Improve the Hydration and Health Status of Individuals with Thin Liquid Aspiration Following Stroke? A Randomized Controlled Trial

The benefit of water protocols for individuals with thin liquid aspiration remains controversial, with mixed findings from a small number of randomized controlled trials (RCTs). This study aimed to contribute to the evidence of the effectiveness of water protocols with a particular emphasis on health outcomes, especially hydration. An RCT was conducted with patients with known thin liquid aspiration post stroke randomized to receiving thickened liquids only or a water protocol. For the 14 participants in rehabilitation facilities whose data proceeded to analysis, there was no difference in the total amount of beverages consumed between the water protocol group (mean = 1103 ml per day, SD = 215 ml) and the thickened liquids only group (mean = 1103 ml, SD = 247 ml). Participants in the water protocol group drank on average 299 ml (SD 274) of water but offset this by drinking less of the thickened liquids. Their hydration improved over time compared with participants in the thickened liquids only group, but differences between groups were not significant. Twenty-one percent of the total sample was diagnosed with dehydration, and no participants in either group were diagnosed with pneumonia. There were significantly more diagnoses of urinary tract infection in the thickened liquids only group compared to the water protocol group (χ 2 = 5.091, p = 0.024), but no differences between groups with regard to diagnoses of dehydration (χ 2 = 0.884, p = 0.347) or constipation (χ 2 = 0.117, p = 0.733). The findings reinforce evidence about the relative safety of water protocols for patients in rehabilitation post stroke and provide impetus for future research into the potential benefits for hydration status and minimizing adverse health outcomes.
Dataless Text Classification: A Topic Modeling Approach with Document Manifold

Recently, dataless text classification has attracted increasing attention. It trains a classifier using seed words of categories, rather than labeled documents that are expensive to obtain. However, a small set of seed words may provide very limited and noisy supervision information, because many documents contain no seed words or only irrelevant seed words. In this paper, we address these issues using document manifold, assuming that neighboring documents tend to be assigned to a same category label. Following this idea, we propose a novel Laplacian seed word topic model (LapSWTM). In LapSWTM, we model each document as a mixture of hidden category topics, each of which corresponds to a distinctive category. Also, we assume that neighboring documents tend to have similar category topic distributions. This is achieved by incorporating a manifold regularizer into the log-likelihood function of the model, and then maximizing this regularized objective. Experimental results show that our LapSWTM significantly outperforms the existing dataless text classification algorithms and is even competitive with supervised algorithms to some extent. More importantly, it performs extremely well when the seed words are scarce.
A head-mounted three dimensional display

The fundamental idea behind the three-dimensional display is to present the user with a perspective image which changes as he moves. The retinal image of the real objects which we see is, after all, only two-dimensional. Thus if we can place suitable two-dimensional images on the observer's retinas, we can create the illusion that he is seeing a three-dimensional object. Although stereo presentation is important to the three-dimensional illusion, it is less important than the change that takes place in the image when the observer moves his head. The image presented by the three-dimensional display must change in exactly the way that the image of a real object would change for similar motions of the user's head. Psychologists have long known that moving perspective images appear strikingly three-dimensional even without stereo presentation; the three-dimensional display described in this paper depends heavily on this "kinetic depth effect."
Topographical and temporal diversity of the human skin microbiome.

Human skin is a large, heterogeneous organ that protects the body from pathogens while sustaining microorganisms that influence human health and disease. Our analysis of 16S ribosomal RNA gene sequences obtained from 20 distinct skin sites of healthy humans revealed that physiologically comparable sites harbor similar bacterial communities. The complexity and stability of the microbial community are dependent on the specific characteristics of the skin site. This topographical and temporal survey provides a baseline for studies that examine the role of bacterial communities in disease states and the microbial interdependencies required to maintain healthy skin.
Faceness-Net: Face Detection through Deep Facial Part Responses

We propose a deep convolutional neural network (CNN) for face detection leveraging on facial attributes based supervision. We observe a phenomenon that part detectors emerge within CNN trained to classify attributes from uncropped face images, without any explicit part supervision. The observation motivates a new method for finding faces through scoring facial parts responses by their spatial structure and arrangement. The scoring mechanism is data-driven, and carefully formulated considering challenging cases where faces are only partially visible. This consideration allows our network to detect faces under severe occlusion and unconstrained pose variations. Our method achieves promising performance on popular benchmarks including FDDB, PASCAL Faces, AFW, and WIDER FACE.
Representation Learning for Grounded Spatial Reasoning

The interpretation of spatial references is highly contextual, requiring joint inference over both language and the environment. We consider the task of spatial reasoning in a simulated environment, where an agent can act and receive rewards. The proposed model learns a representation of the world steered by instruction text. This design allows for precise alignment of local neighborhoods with corresponding verbalizations, while also handling global references in the instructions. We train our model with reinforcement learning using a variant of generalized value iteration. The model outperforms state-of-the-art approaches on several metrics, yielding a 45% reduction in goal localization error.
Deep Neural Networks for Czech Multi-label Document Classification

This paper is focused on automatic multi-label document classification of Czech text documents. The current approaches usually use some preprocessing which can have negative impact (loss of information, additional implementation work, etc). Therefore, we would like to omit it and use deep neural networks that learn from simple features. This choice was motivated by their successful usage in many other machine learning fields. Two different networks are compared: the first one is a standard multi-layer perceptron, while the second one is a popular convolutional network. The experiments on a Czech newspaper corpus show that both networks significantly outperform baseline method which uses a rich set of features with maximum entropy classifier. We have also shown that convolutional network gives the best results.
Multidisciplinary design optimization of mechanisms

The optimization of mechanisms is usually done in the field of mechanism itself. As a result the structural safety of the mechanism is neglected. To ensure the safety and improve the dynamic characteristics of mechanisms, a multidisciplinary design optimization procedure is proposed in this paper to synthesize optimum mechanisms. Two disciplines are involved in the multidisciplinary design optimization. They are the mechanism and the structure. The multi-level decomposition approach is chosen to generate optimum mechanisms. The optimized mechanisms not only satisfy mechanism and structural constraints but also have optimum objective function values in the two disciplines. In order to solve general mechanism design problems two widely used commercial softwares MSC/NASTRAN and MSC/ADAMS are integrated in the procedure to do the structural and the mechanism analysis, respectively. When the structural optimization is performed, a compromised approach is introduced to treat multiple configurations of mechanisms during operation. Two mechanism design problems are given to test the proposed method.
Random telegraph noise (RTN) in scaled RRAM devices

The random telegraph noise (RTN) related read instability in resistive random access memory (RRAM) is evaluated by employing the RTN peak-to-peak (P-p) amplitude as a figure of merit (FoM). Variation of the FoM value over multiple set/reset cycles is found to follow the log-normal distribution. P-p decreases with the reduction of the read current, which allows scaling of the RRAM operating current. The RTN effect is attributed to the mechanism of activation/deactivation of the electron traps in (in HRS) or near (in LRS) the filament that affects the current through the RRAM device.
Neural Monkey: The Current State and Beyond

Neural Monkey is an open-source toolkit for sequence-to-sequence learning. The focus of this paper is to present the current state of the toolkit to the intended audience, which includes students and researchers, both active in the deep learning community and newcomers. For each of these target groups, we describe the most relevant features of the toolkit, including the simple configuration scheme, methods of model inspection that promote useful intuitions, or a modular design for easy prototyping. We summarize relevant contributions to the research community which were made using this toolkit and discuss the characteristics of our toolkit with respect to other existing systems. We conclude with a set of proposals for future development.
Phishing e-mail detection by using deep learning algorithms

Phishing e-mails are considered as spam e-mails, which aim to collect sensitive personal information about the users via network. Since the main purpose of this behavior is mostly to harm users financially, it is vital to detect these phishing or spam e-mails immediately to prevent unauthorized access to users' vital information. To detect phishing e-mails, using a quicker and robust classification method is important. Considering the billions of e-mails on the Internet, this classification process is supposed to be done in a limited time to analyze the results. In this work, we present some of the early results on the classification of spam email using deep learning and machine methods. We utilize word2vec to represent emails instead of using the popular keyword or other rule-based methods. Vector representations are then fed into a neural network to create a learning model. We have tested our method on an open dataset and found over 96% accuracy levels with the deep learning classification methods in comparison to the standard machine learning algorithms.
DIRECTED-INFO GAIL: LEARNING HIERARCHICAL POLICIES

The use of imitation learning to learn a single policy for a complex task that has multiple modes or hierarchical structure can be challenging. In fact, previous work has shown that when the modes are known, learning separate policies for each mode or sub-task can greatly improve the performance of imitation learning. In this work, we discover the interaction between sub-tasks from their resulting stateaction trajectory sequences using a directed graphical model. We propose a new algorithm based on the generative adversarial imitation learning framework which automatically learns sub-task policies from unsegmented demonstrations. Our approach maximizes the directed information flow in the graphical model between sub-task latent variables and their generated trajectories. We also show how our approach connects with the existing Options framework, which is commonly used to learn hierarchical policies.
Comparing and evaluating the sentiment on newspaper articles: A preliminary experiment

Recent years have brought a symbolic growth in the volume of research in Sentiment Analysis, mostly on highly subjective text types like movie or product reviews. The main difference these texts have with news articles is that their target is apparently defined and unique across the text. Thence while dealing with news articles, we performed three subtasks namely identifying the target; separation of good and bad news content from the good and bad sentiment expressed on the target and analysis of clearly marked opinion that is expressed explicitly, not needing interpretation or the use of world knowledge. On concluding these tasks, we present our work on mining opinions about three different Indian political parties during elections in the year 2009. We built a Corpus of 689 opinion-rich instances from three different English dailies namely The Hindu, Times of India and Economic Times extracted from 02/ 01/ 2009 to 05/ 01/ 2009 (MM/ DD/ YY). In which (a) we tested the relative suitability of various sentiment analysis methods (both machine learning and lexical based) and (b) we attempted to separate positive or negative opinion from good or bad news. Evaluation includes comparison of three sentiment analysis methods (two machine learning based and one lexical based) and analyzing the choice of certain words used in political text which influence the Sentiments of public in polls. This preliminary experiment will benefit in predicting and forecasting the winning party in forthcoming Indian elections 2014.
Context-aware review helpfulness rating prediction

Online reviews play a vital role in the decision-making process for online users. Helpful reviews are usually buried in a large number of unhelpful reviews, and with the consistently increasing number of reviews, it becomes more and more difficult for online users to find helpful reviews. Therefore most online review websites allow online users to rate the helpfulness of a review and a global helpfulness score is computed for the review based on its available ratings. However, in reality, user-specified helpfulness ratings for reviews are very sparse - a few reviews attract large numbers of helpfulness ratings while most reviews obtain few or even no helpfulness ratings. The available helpfulness ratings are too sparse for online users to assess the helpfulness of reviews. Also the helpfulness of a review is not necessarily equally useful for all users and users with different background may treat the helpfulness of a review very differently. The user idiosyncracy of review helpfulness motivates us to study the problem of review helpfulness rating prediction in this paper. We first identify various types of context information, model them mathematically, and propose a context-aware review helpfulness rating prediction framework CAP. Experimental results demonstrate the effectiveness of the proposed framework and the importance of context awareness in solving the review helpfulness rating prediction problem.
WIST: Wi-SUN FAN Protocol Emulation Testbed

We present a novel emulation testbed design to test wireless protocols in an efficient, realistic, and reproducible manner, and use Wi-SUN Field Area Network (FAN) protocols to demonstrate its efficiency. The testbed provides emulated Layer 2 connectivity for a mesh of wireless nodes without needing to manipulate Layer 1 signals or inject radio frequency interference. An early version of the testbed that relied on a centralized server to forward packets introduced excessive differential delay that interfered with protocol operation. The improved version described in this paper achieves near-simultaneous delivery of a packet to the set of nodes within range of a sender. To provide an emulated delivery mechanism, the testbed uses multicast addressing in an unusual way. Each node is assigned a unique bit position within the multicast address space. Instead of sending outgoing packets to an emulation server, a node forms a multicast address with bits of the address set for all nodes within range, and multicasts the packet. Conceptually, each node is configured to accept a large set of multicast destination addresses that correspond to transmissions from neighbors in the emulated mesh topology. In practice, the testbed achieves packet recognition efficiently, without requiring a network interface to handle large numbers of multicast addresses. As a result, all neighbors of a node in the emulated mesh topology receive a copy of a packet at approximately the same time, like an actual wireless network. The paper also describes a software-defined programmable management system that uses scripted testbed commands to run sequences of tests.
ENHANCEMENT STRATEGIES FOR FRAME-TO-FRAME UAS STEREO VISUAL ODOMETRY

Autonomous navigation of indoor unmanned aircraft systems (UAS) requires accurate pose estimations usually obtained from indirect measurements. Navigation based on inertial measurement units (IMU) is known to be affected by high drift rates. The incorporation of cameras provides complementary information due to the different underlying measurement principle. The scale ambiguity problem for monocular cameras is avoided when a light-weight stereo camera setup is used. However, also frame-to-frame stereo visual odometry (VO) approaches are known to accumulate pose estimation errors over time. Several valuable real-time capable techniques for outlier detection and drift reduction in frame-to-frame VO, for example robust relative orientation estimation using random sample consensus (RANSAC) and bundle adjustment, are available. This study addresses the problem of choosing appropriate VO components. We propose a frame-to-frame stereo VO method based on carefully selected components and parameters. This method is evaluated regarding the impact and value of different outlier detection and drift-reduction strategies, for example keyframe selection and sparse bundle adjustment (SBA), using reference benchmark data as well as own real stereo data. The experimental results demonstrate that our VO method is able to estimate quite accurate trajectories. Feature bucketing and keyframe selection are simple but effective strategies which further improve the VO results. Furthermore, introducing the stereo baseline constraint in pose graph optimization (PGO) leads to significant improvements.  Corresponding author
Finding and tracking road lanes using "line-snakes"

This paper presents a method forfinding and tracking road lanes. The method extracts and tracks lane boundaries for vision-guided vehicle navigation by combining the hough transform and the “active line model ( A M ) ” . The hough transform can extract vanishing points of the road, which can be used as a good estimation of the vehicle heading. For the curved road, however, the estimation may be too crude to be used for navigation. Therefore, the hough transform is used to obtain an initial position estimation of the lane boundaries on the road. The line snake ALM then improves the initial approximation to an accurate configuration of the lane boundaries. Once the line snake is initialized in the first image, it tracks the road lanes using the external and internal forces computed from images and a proposed boundary refinement technique. Specifically, an image region is divided into a few subregions along the vertical direction. The hough transform is then performed for each sub-region and candidate lines of road lanes in each sub-region are extracted. Among candidate lines, a most prominent line is found by the ALM that minimizes a defined snake energy. The external energy of ALM is a normalized sum of image gradients along the line. The internal deformation energy ensures the continuity of two neighboring lines by using the angledifference between two adjacent lines and the distance between the two lines. A search method based on the dynamic programming reduces the computational cost. The proposed method gives a uniJied framework for detecting, refining and tracking the road lane. Experimental results using images of a real road scene are presented.
Agile distributed software development: enacting control through media and context

While face-to-face interaction is fundamental in agile software development, distributed environments must rely extensively on mediated interactions. Practicing agile principles in distributed environments therefore poses particular control challenges related to balancing fixed vs. evolving quality requirements and people vs. process-based collaboration. To investigate these challenges, we conducted an in-depth case study of a successful agile distributed software project with participants from a Russian firm and a Danish firm. Applying Kirsch’s elements of control framework, we offer an analysis of how control was enacted through the project context and in the participants’ mediated communication. The analysis reveals that formal measurement and evaluation control were persistently enacted through mediated communication. These formal control practices were, however, predominantly carried out in conjunction with informal roles and relationships such as clan-like control inherent in agile development. Overall, the study demonstrates that, if appropriately applied, communication technologies can significantly support distributed, agile practices by allowing concurrent enactment of both formal and informal controls. The paper discusses these findings as they relate to previous research and concludes with their implications for future research.
AN OVERVIEW OF MASSIVE MIMO SYSTEM IN 5 G

4G is proving good speeds up to 1Gbps. Then why do we need anything more. The problem is that it is not able to provide real time applications. 5G is the name given to the next generation of mobile data connectivity. It will definitely provide great speeds between 10Gbps to 100Gbps and it will have enough capacity. But the thing that separated 5G from 4G is latency; the latency provided by 4G is between 40ms to 60ms, whereas in 5G it will provide ultra latency between 1ms to 10ms. The standards for 5G will be set till 2020 and it will be applicable by 2022/23. In this paper we have discussed about 5G and its advantages, a probable architecture for 5G and some challenges in 5G. The main technology that maybe used in 5G are massive MIMO, millimetre wave communication, device to device communication, beam division multiple access etc. In this paper we have discussed about massive MIMO, channel estimate in massive MIMO, beam division multiple access technique to be used in massive MIMO, antenna selection in massive MIMO, capacity and energy efficiency in massive MIMO. In future 5G is going to be a technology which will be invisible, I will be just there everywhere just like electricity. It is a very good area for research as standards and frequency band for 5G are yet to be standardised.