nils-herrmann/clean_scirepeval huggingface dataset:
Exploring IOT Application Using Raspberry Pi

There are thousands of sensors in an industry with different usage, such as, pressure transmitters, flow meter, temperature transmitters, level transmitters, and so on. Wired networks are mainly used to transfer data to base station by connecting sensor. It brings advantage as it provides reliable and stable communication system for instruments and controls. However, the cost of cables necessary is very costly. Therefore, recently low cost wireless networks are strongly required by customers, for example, temporary instrument networks and/or some non-critical permanent sites which require low data rate and longer battery life. In client/server model, file server act as a parent’s node which allow multiple child node to connect with it. It is responsible for central storage and data management so that other computers enable to access the file under the same network. This article explores the use of Raspberry Pi to function as a server in which several laptops are connected to it to copy, store and delete the file over network. IT requires authentication for user login before granting access to the file to ensure data integrity and security. File server is widely used in many areas, for example in education for uploading study note into the serve and student immediate downloading it into their computer. Moreover this work also explores the use of Raspberry Pi B+ model and XBee (ZigBee module) to demonstrate wireless communication data transmission, proving the validity of usage as a mobile low-power wireless network communication. The main goal of the research is to explore the use of Raspberry Pi for client-server communication using various wireless communication scenario such as Wi-Fi and ZigBee. Index Terms – client/server model, Raspberry Pi, XBee.
Information technology and organizational learning: a review and assessment of research

This paper reviews and assesses the emerging research literature on information technology and organizational learning. After discussing issues of meaning and measurement, we identify and assess two main streams of research: studies that apply organizational learning concepts to the process of implementing and using information technology in organizations; and studies concerned with the design of information technology applications to support organizational learning. From the former stream of research, we conclude that experience plays an important, yet indeterminate role in implementation success; learning is accomplished through both formal training and participation in practice; organizational knowledge barriers may be overcome by learning from other organizations; and that learning new technologies is a dynamic process characterized by relatively narrow windows of opportunity. From the latter stream, we conclude that conceptual designs for organizational memory information systems are a valuable contribution to artifact development; learning is enhanced through systems that support communication and discourse; and that information technologies have the potential to both enable and disable organizational learning. Currently, these two streams flow independently of each other, despite their close conceptual and practical links. We advise that future research on information technology and organizational learning proceeds in a more integrated fashion, recognizes the situated nature of organizational learning, focuses on distributed organizational memory, demonstrates the effectiveness of artifacts in practice, and looks for relevant research findings in related fields.
Optimization for allocating BEV recharging stations in urban areas by using hierarchical clustering

The Battery Electric Vehicle (BEV) prototypes are evolving into a reality in a foreseeable future. Before BEV can embark on a mass adoption by road commuters, a new infrastructure for battery charging will have to be ready in place. By 2015, access to BEV charging will be available at nearly one million charge points in the United States, as claimed by CleanMPG. Early BEV adopters will primarily charge their vehicles at home, in their private garages. However, for many Asia-Pacific regions where people live in concrete ghettos, public charging will play a more central role due to reduced access to convenient home charging. This research focuses on planning BEV charging locations to be installed in urbanized areas where they are usually characterized by dense traffic concentrations, restricted street spaces and other complex factors such as the distribution of power grids. We proposed a two-steps model that first quantified the road information into data points, and subsequently they are converged into 'demand clusters' over an urbanized area by hierarchical clustering analysis. Optimization techniques then applied on the demand clusters with the aim of meeting the supplies and demands, while at the same time certain constraints and cost factors are considered. The model is believed to be an important decision-support tool for city planning and BEV charging stations allocation.
Minimal Interaction Content Discovery in Recommender Systems

Many prior works in recommender systems focus on improving the accuracy of item rating predictions. In comparison, the areas of recommendation interfaces and user-recommender interaction remain underexplored. In this work, we look into the interaction of users with the recommendation list, aiming to devise a method that simplifies content discovery and minimizes the cost of reaching an item of interest. We quantify this cost by the number of user interactions (clicks and scrolls) with the recommendation list. To this end, we propose generalized linear search (GLS), an adaptive combination of the established linear and generalized search (GS) approaches. GLS leverages the advantages of these two approaches, and we prove formally that it performs at least as well as GS. We also conduct a thorough experimental evaluation of GLS and compare it to several baselines and heuristic approaches in both an offline and live evaluation. The results of the evaluation show that GLS consistently outperforms the baseline approaches and is also preferred by users. In summary, GLS offers an efficient and easy-to-use means for content discovery in recommender systems.
INTRINSIC AND EXTRINSIC MOTIVATION 1

A central tenet of economics is that individuals respond to incentives. For psychologists and sociologists, by contrast, rewards and punishments are often counterproductive, because they undermine “intrinsic motivation”. We reconcile these two views, showing how performance incentives offered by an informed principal (manager, teacher, parent) can adversely impact an agent’s (worker, child) perception of the task, or of his own abilities. Incentives are then only weak reinforcers in the short run, and negative reinforcers in the long run. We also study the effects of empowerment, help and excuses on motivation, as well as situations of ego-bashing reflecting a battle for dominance within a relationship.
Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems

Social network websites, such as Facebook, YouTube, Lastfm etc, have become a popular platform for users to connect with each other and share content or opinions. They provide rich information for us to study the influence of user’s social circle in their decision process. In this paper, we are interested in examining the effectiveness of social network information to predict the user’s ratings of items. We propose a novel hierarchical Bayesian model which jointly incorporates topic modeling and probabilistic matrix factorization of social networks. A major advantage of our model is to automatically infer useful latent topics and social information as well as their importance to collaborative filtering from the training data. Empirical experiments on two large-scale datasets show that our algorithm provides a more effective recommendation system than the state-of-the art approaches. Our results reveal interesting insight that the social circles have more influence on people’s decisions about the usefulness of information (e.g., bookmarking preference on Delicious) than personal taste (e.g., music preference on Lastfm). We also examine and discuss solutions on potential information leak in many recommendation systems that utilize social information. Appearing in Proceedings of the 29 th International Conference on Machine Learning, Edinburgh, Scotland, UK, 2012.
The Mini Nutritional Assessment (MNA) review of the literature--What does it tell us?

To review the literature on the MNA to Spring 2006, we searched MEDLINE, Web of Science and Scopus, and did a manual search in J Nutr Health Aging, Clin Nutr, Eur J Clin Nutr and free online available publications.



The MNA was validated against two principal criteria, clinical status and comprehensive nutrition assessment using principal component and discriminant analysis. The MNA shortform (MNA-SF) was developed and validated to allow a 2-step screening process. The MNA and MNA-SF are sensitive, specific, and accurate in identifying nutrition risk.



The prevalence of malnutrition in community-dwelling elderly (21 studies, n = 14149 elderly) is 2 +/- 0.1% (mean +/- SE, range 0- 8%) and risk of malnutrition is 24 +/- 0.4% (range 8-76%). A similar pattern is seen in out-patient and home care elderly (25 studies, n = 3119 elderly) with prevalence of undernutrition 9 +/- 0.5% (mean +/- SE, range 0-30%) and risk of malnutrition 45 +/- 0.9% (range 8-65%). A high prevalence of undernutrition has been reported in hospitalized and institutionalized elderly patients: prevalence of malnutrition is 23 +/- 0.5% (mean +/- SE, range 1- 74%) in hospitals (35 studies, n = 8596) and 21 +/- 0.5% (mean +/- SE, range 5-71%) in institutions (32 studies, n = 6821 elderly). An even higher prevalence of risk of malnutrition was observed in the same populations, with 46 +/- 0.5% (range 8-63%) and 51 +/- 0.6% (range 27-70%), respectively. In cognitively impaired elderly subjects (10 studies, n = 2051 elderly subjects), detection using the MNA, prevalence of malnutrition was 15 +/- 0.8% (mean +/- SE, range 0-62%), and 44 +/- 1.1% (range 19-87%) of risk of malnutrition.


CHARACTERISTICS
The large variability is due to differences in level of dependence and health status among the elderly. In hospital settings, a low MNA score is associated with an increase in mortality, prolonged length of stay and greater likelihood of discharge to nursing homes. Malnutrition is associated with functional and cognitive impairment and difficulties eating. The MNA(R) detects risk of malnutrition before severe change in weight or serum proteins occurs.



Intervention studies demonstrate that timely intervention can stop weight loss in elderly at risk of malnutrition or undernourished and is associated with improvements in MNA scores. The MNA can also be used as a follow up assessment tool.



The MNA is a screening and assessment tool with a reliable scale and clearly defined thresholds, usable by health care professionals. It should be included in the geriatric assessment and is proposed in the minimum data set for nutritional interventions.
Ridge-based vessel segmentation in color images of the retina

A method is presented for automated segmentation of vessels in two-dimensional color images of the retina. This method can be used in computer analyses of retinal images, e.g., in automated screening for diabetic retinopathy. The system is based on extraction of image ridges, which coincide approximately with vessel centerlines. The ridges are used to compose primitives in the form of line elements. With the line elements an image is partitioned into patches by assigning each image pixel to the closest line element. Every line element constitutes a local coordinate frame for its corresponding patch. For every pixel, feature vectors are computed that make use of properties of the patches and the line elements. The feature vectors are classified using a kNN-classifier and sequential forward feature selection. The algorithm was tested on a database consisting of 40 manually labeled images. The method achieves an area under the receiver operating characteristic curve of 0.952. The method is compared with two recently published rule-based methods of Hoover et al. and Jiang et al. . The results show that our method is significantly better than the two rule-based methods (p<0.01). The accuracy of our method is 0.944 versus 0.947 for a second observer.
AJAX Crawl: Making AJAX Applications Searchable

Current search engines such as Google and Yahoo! are prevalent for searching the Web. Search on dynamic client-side Web pages is, however, either inexistent or far from perfect, and not addressed by existing work, for example on Deep Web. This is a real impediment since AJAX and Rich Internet Applications are already very common in the Web. AJAX applications are composed of states which can be seen by the user, but not by the search engine, and changed by the user using client-side events. Current search engines either ignore AJAX applications or produce false negatives. The reason is that crawling client-side code is a difficult problem that cannot be solved naively by invoking user events. The challenges are: lack of caching, duplicate states detection, very granular events, reducing the number of AJAX calls and infinite event invocation. This paper sets the stage for this new search challenge and proposes a solution: it shows how an AJAX Web application can be crawled in the granularity of the application states. A model of AJAX Web sites is presented. An AJAX Crawler and optimizations for caching and duplicate elimination are defined, and finally, the gain in search result quality and corresponding performance price are evaluated on YouTube, a real AJAX application.
Inside "Big Data management": ogres, onions, or parfaits?

In this paper we review the history of systems for managing "Big Data" as well as today's activities and architectures from the (perhaps biased) perspective of three "database guys" who have been watching this space for a number of years and are currently working together on "Big Data" problems. Our focus is on architectural issues, and particularly on the components and layers that have been developed recently (in open source and elsewhere) and on how they are being used (or abused) to tackle challenges posed by today's notion of "Big Data". Also covered is the approach we are taking in the ASTERIX project at UC Irvine, where we are developing our own set of answers to the questions of the "right" components and the "right" set of layers for taming the "Big Data" beast. We close by sharing our opinions on what some of the important open questions are in this area as well as our thoughts on how the dataintensive computing community might best seek out answers.
Paraphrasing for Style

We present initial investigation into the task of paraphrasing language while targeting a particular writing style. The plays of William Shakespeare and their modern translations are used as a testbed for evaluating paraphrase systems targeting a specific style of writing. We show that even with a relatively small amount of parallel training data, it is possible to learn paraphrase models which capture stylistic phenomena, and these models outperform baselines based on dictionaries and out-of-domain parallel text. In addition we present an initial investigation into automatic evaluation metrics for paraphrasing writing style. To the best of our knowledge this is the first work to investigate the task of paraphrasing text with the goal of targeting a specific style of writing.
Comparison of Feedback Control Techniques for Torque-Vectoring Control of Fully Electric Vehicles

Fully electric vehicles (FEVs) with individually controlled powertrains can significantly enhance vehicle response to steering-wheel inputs in both steady-state and transient conditions, thereby improving vehicle handling and, thus, active safety and the fun-to-drive element. This paper presents a comparison between different torque-vectoring control structures for the yaw moment control of FEVs. Two second-order sliding-mode controllers are evaluated against a feedforward controller combined with either a conventional or an adaptive proportional-integral-derivative (PID) controller. Furthermore, the potential performance and robustness benefits arising from the integration of a body sideslip controller with the yaw rate feedback control system are assessed. The results show that all the evaluated controllers are able to significantly change the understeer behavior with respect to the baseline vehicle. The PID-based controllers achieve very good vehicle performance in steady-state and transient conditions, whereas the controllers based on the sliding-mode approach demonstrate a high level of robustness against variations in the vehicle parameters. The integrated sideslip controller effectively maintains the sideslip angle within acceptable limits in the case of an erroneous estimation of the tire-road friction coefficient.
Two-channel user authentication by using USB on Cloud

According to parallel computing technology, Cloud service is popular, and it is easy to use Cloud service at everywhere. Cloud means involving application systems that are executed within the cloud and operated via the internet enabled devices. Cloud computing does not rely on the use of cloud storage as it will be removed upon users download action. Clouds can be classified as public, private and hybrid. Cloud service comes up with Ubiquitous; Cloud service users can use their service at anywhere at any time. It is convenient. However, there is a tradeoff. If user’s username and password are compromised, user’s cloud system is in danger, and their confidential information will be in jeopardy. At anywhere and anytime with any device, Cloud user’s credential could be in jeopardy. Security concerns in Cloud play a major role. It is the biggest obstacle to developing in Cloud. However, Cloud is still popular and vulnerability for hacking because of one channel user authentication. Therefore, this research proposes two-channel user authentication by using USB to emphasise security.
Scene Understanding by Reasoning Stability and Safety

This paper presents a new perspective for 3D scene understanding by reasoning object stability and safety using intuitive mechanics. Our approach utilizes a simple observation that, by human design, objects in static scenes should be stable in the gravity field and be safe with respect to various physical disturbances such as human activities. This assumption is applicable to all scene categories and poses useful constraints for the plausible interpretations (parses) in scene understanding. Given a 3D point cloud captured for a static scene by depth cameras, our method consists of three steps: (i) recovering solid 3D volumetric primitives from voxels; (ii) reasoning stability by grouping the unstable primitives to physically stable objects by optimizing the stability and the scene prior; and (iii) reasoning safety by evaluating the physical risks for objects under physical disturbances, such as human activity, wind or earthquakes. We adopt a novel intuitive physics model and represent the energy landscape of each primitive and object in the scene by a disconnectivity graph (DG). We construct a contact graph with nodes being 3D volumetric primitives and edges representing the supporting relations. Then we adopt a Swendson–Wang Cuts algorithm to partition the contact graph into groups, each of which is a stable object. In order to detect unsafe objects in a static scene, our method further infers hidden and situated causes (disturbances) in the scene, and then introduces intuitive physical mechanics to predict possible effects (e.g., falls) as consequences of the disturbances. In experiments, we demonstrate that the algorithm achieves a substantially better performance for (i) object segmentation, (ii) 3D volumetric recovery, and (iii) scene understanding with respect to other state-of-the-art methods. We also compare the safety prediction from the intuitive mechanics model with human judgement.
A New Vision for Smart Objects and the Internet of Things: Mobile Robots and Long-Range UHF RFID Sensor Tags

We present a new vision for smart objects and the Internet of Things wherein mobile robots interact with wirelessly-powered, long-range, ultra-high frequency radio frequency identification (UHF RFID) tags outfitted with sensing capabilities. We explore the technology innovations driving this vision by examining recently-commercialized sensor tags that could be affixed-to or embedded-in objects or the environment to yield true embodied intelligence. Using a pair of autonomous mobile robots outfitted with UHF RFID readers, we explore several potential applications where mobile robots interact with sensor tags to perform tasks such as: soil moisture sensing, remote crop monitoring, infrastructure monitoring, water quality monitoring, and remote sensor deployment.
Integration challenges of intelligent transportation systems with connected vehicle, cloud computing, and internet of things technologies

Transportation is a necessary infrastructure for our modern society. The performance of transportation systems is of crucial importance for individual mobility, commerce, and for the economic growth of all nations. In recent years modern society has been facing more traffic jams, higher fuel prices, and an increase in CO2 emissions. It is imperative to improve the safety and efficiency of transportation. Developing a sustainable intelligent transportation system requires the seamless integration and interoperability with emerging technologies such as connected vehicles, cloud computing, and the Internet of Things. In this article we present and discuss some of the integration challenges that must be addressed to enable an intelligent transportation system to address issues facing the transportation sector such as high fuel prices, high levels of CO2 emissions, increasing traffic congestion, and improved road safety.
Automatic Code Completion

Code completion software is an important tool for many developers, but it traditionally fails to model any long term program dependencies such as scoped variables, instead settling for suggestions based on static libraries. In this paper we present a deep learning approach to code completion for non-terminals (program structural components) and terminals (program text) that takes advantage of running dependencies to improve predictions. We develop an LSTM model and augment it with several approaches to Attention in order to better capture the relative value of the input, hidden state, and context. After evaluating on a large dataset of JavaScript programs, we demonstrate that our Gated LSTM model significantly improves over a Vanilla LSTM baseline, achieving an accuracy of 77% on the non-terminal prediction problem and 46% accuracy on the terminal prediction problem.
Nonexistence of Certain Symmetrical Spherical Codes

A spherical 1-code Wis any finite subset of the unit sphere in n dimensions S n-1 for which d(u, v) > I for every u, v from W, u ~ v. A spherical 1-code is symmetric ifu ~ Wimplies u fi W. The best upper bounds on the size of symmetric spherical codes on S n-~ were obtained in . Here we obtain the same bounds by a similar method and improve these bounds for n = 5, 10, 14 and 22.