nils-herrmann/clean_scirepeval huggingface dataset:
Large-scale modular quantum-computer architecture with atomic memory and photonic interconnects

C. Monroe,1 R. Raussendorf,2 A. Ruthven,2 K. R. Brown,3 P. Maunz,4,* L.-M. Duan,5 and J. Kim4 1Joint Quantum Institute, University of Maryland Department of Physics and National Institute of Standards and Technology, College Park, Maryland 20742, USA 2Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia V6T1Z1, Canada 3Schools of Chemistry and Biochemistry; Computational Science and Engineering; and Physics, Georgia Institute of Technology, Atlanta, Georgia 30332, USA 4Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina 27708, USA 5Department of Physics and MCTP, University of Michigan,  2013;  February 2014)
Lossless Convexification of Nonconvex Control Bound and Pointing Constraints of the Soft Landing Optimal Control Problem

Planetary soft landing is one of the benchmark problems of optimal control theory and is gaining renewed interest due to the increased focus on the exploration of planets in the solar system, such as Mars. The soft landing problem with all relevant constraints can be posed as a finite-horizon optimal control problem with state and control constraints. The real-time generation of fuel-optimal paths to a prescribed location on a planet's surface is a challenging problem due to the constraints on the fuel, the control inputs, and the states. The main difficulty in solving this constrained problem is the existence of nonconvex constraints on the control input, which are due to a nonzero lower bound on the control input magnitude and a nonconvex constraint on its direction. This paper introduces a convexification of the control constraints that is proven to be lossless; i.e., an optimal solution of the soft landing problem can be obtained via solution of the proposed convex relaxation of the problem. The lossless convexification enables the use of interior point methods of convex optimization to obtain optimal solutions of the original nonconvex optimal control problem.
Machine Learning Algorithms in Heavy Process Manufacturing

In a global economy, manufacturers mainly compete with cost efficiency of production, as the price of raw materials are similar worldwide. Heavy industry has two big issues to deal with. On the one hand there is lots of data which needs to be analyzed in an effective manner, and on the other hand making big improvements via investments in cooperate structure or new machinery is neither economically nor physically viable. Machine learning offers a promising way for manufacturers to address both these problems as they are in an excellent position to employ learning techniques with their massive resource of historical production data. However, choosing modelling a strategy in this setting is far from trivial and this is the objective of this article. The article investigates characteristics of the most popular classifiers used in industry today. Support Vector Machines, Multilayer Perceptron, Decision Trees, Random Forests, and the meta-algorithms Bagging and Boosting are mainly investigated in this work. Lessons from real-world implementations of these learners are also provided together with future directions when different learners are expected to perform well. The importance of feature selection and relevant selection methods in an industrial setting are further investigated. Performance metrics have also been discussed for the sake of completion.
Hadoop: Bitcoin-BlockChain - A New Era Needed In Distributed Computing

Today, with the use of Internet, a huge volume of data been generated in the form of transactions, logs etc. As assessed, 90% of total volume of data generated since evaluation of Computers is from last 3 years only. It’s because of advancements in Data storage, global connectivity with Internet high speed, mobile applications usage and IoT. BigData Technologies aims at processing the BigData for deriving trend analysis and business usage from its BigData information. This paper highlights some of the security concerns that Hadoop implemented in its current version and need for some of the enhancements along with a new methodology such as Electronic Currency (BitCoin) and BlockChain functionality. And also emphasises on why and how BitCoin and BlockChain can fit in Hadoop Eco-Systems and their possible advantages and disadvantages. Especially, in validating and authorizing business transactions with some mathematical cryptographic techniques like hashcode with the help of BlockChain Miners.
Image De-raining Using a Conditional Generative Adversarial Network

Severe weather conditions such as rain and snow adversely affect the visual quality of images captured under such conditions thus rendering them useless for further usage and sharing. In addition, such degraded images drastically affect performance of vision systems. Hence, it is important to solve the problem of single image de-raining/de-snowing. However, this is a difficult problem to solve due to its inherent ill-posed nature. Existing approaches attempt to introduce prior information to convert it into a well-posed problem. In this paper, we investigate a new point of view in addressing the single image de-raining problem. Instead of focusing only on deciding what is a good prior or a good framework to achieve good quantitative and qualitative performance, we also ensure that the de-rained image itself does not degrade the performance of a given computer vision algorithm such as detection and classification. In other words, the de-rained result should be indistinguishable from its corresponding clear image to a given discriminator. This criterion can be directly incorporated into the optimization framework by using the recently introduced conditional generative adversarial networks (GANs). To minimize artifacts introduced by GANs and ensure better visual quality, a new refined loss function is introduced. Based on this, we propose a novel single image de-raining method called Image De-raining Conditional General Adversarial Network (ID-CGAN), which considers quantitative, visual and also discriminative performance into the objective function. Experiments evaluated on synthetic images and real images show that the proposed method outperforms many recent state-of-the-art single image de-raining methods in terms of quantitative and visual performance.
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis

Reducing the dimensionality of data without losing intrinsic information is an important preprocessing step in high-dimensional data analysis. Fisher discriminant analysis (FDA) is a traditional technique for supervised dimensionality reduction, but it tends to give undesired results if samples in a class are multimodal. An unsupervised dimensionality reduction method called localitypreserving projection (LPP) can work well with multimodal data due to its locality preserving property. However, since LPP does not take the label information into account, it is not necessarily useful in supervised learning scenarios. In this paper, we propose a new linear supervised dimensionality reduction method called local Fisher discriminant analysis (LFDA), which effectively combines the ideas of FDA and LPP. LFDA has an analytic form of the embedding transformation and the solution can be easily computed just by solving a generalized eigenvalue problem. We demonstrate the practical usefulness and high scalability of the LFDA method in data visualization and classification tasks through extensive simulation studies. We also show that LFDA can be extended to non-linear dimensionality reduction scenarios by applying the kernel trick.
Electronic bullying among middle school students.

Electronic communications technologies are affording children and adolescents new means of bullying one another. Referred to as electronic bullying, cyberbullying, or online social cruelty, this phenomenon includes bullying through e-mail, instant messaging, in a chat room, on a website, or through digital messages or images sent to a cell phone. The present study examined the prevalence of electronic bullying among middle school students.



A total of 3,767 middle school students in grades 6, 7, and 8 who attend six elementary and middle schools in the southeastern and northwestern United States completed a questionnaire, consisting of the Olweus Bully/Victim Questionnaire and 23 questions developed for this study that examined participants' experiences with electronic bullying, as both victims and perpetrators.



Of the students, 11% that they had been electronically bullied at least once in the last couple of months (victims only); 7% indicated that they were bully/victims; and 4% had electronically bullied someone else at least once in the previous couple of months (bullies only). The most common methods for electronic bullying (as reported by both victims and perpetrators) involved the use of instant messaging, chat rooms, and e-mail. Importantly, close to half of the electronic bully victims reported not knowing the perpetrator's identity.



Electronic bullying represents a problem of significant magnitude. As children's use of electronic communications technologies is unlikely to wane in coming years, continued attention to electronic bullying is critical. Implications of these findings for youth, parents, and educators are discussed.
Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification

Learning generic and robust feature representations with data from multiple domains for the same problem is of great value, especially for the problems that have multiple datasets but none of them are large enough to provide abundant data variations. In this work, we present a pipeline for learning deep feature representations from multiple domains with Convolutional Neural Networks (CNNs). When training a CNN with data from all the domains, some neurons learn representations shared across several domains, while some others are effective only for a specific one. Based on this important observation, we propose a Domain Guided Dropout algorithm to improve the feature learning procedure. Experiments show the effectiveness of our pipeline and the proposed algorithm. Our methods on the person re-identification problem outperform stateof-the-art methods on multiple datasets by large margins.
HTTPi for Practical End-to-End Web Content Integrity

Widespread growth of open wireless hotspots has made it easy to carry out man-in-the-middle attacks and impersonate web sites. Although HTTPS can be used to prevent such attacks, its universal adoption is hindered by its performance cost and its inability to leverage caching at intermediate servers (such as CDN servers and caching proxies) while maintaining end-to-end security. To complement HTTPS, we revive an old idea from SHTTP, a protocol that offers end-to-end web integrity without confidentiality. We name the protocol HTTPi and give it an efficient design that is easy to deploy for today’s web. In particular, we tackle several previously-unidentified challenges, such as supporting progressive page loading on the client’s browser, handling mixed content, and defining access control policies among HTTP, HTTPi, and HTTPS content from the same domain. Our prototyping and evaluation experience show that HTTPi incurs negligible performance overhead over HTTP, can leverage existing web infrastructure such as CDNs or caching proxies without any modifications to them, and can make many of the mixed-content problems in existing HTTPS web sites easily go away. Based on this experience, we advocate browser and web server vendors to adopt HTTPi.
Time Complexity of K-Means and K-Medians Clustering Algorithms in Outliers Detection

Data mining responsibilities show the broad features of the data in the database and also examine the current data in order to determine some arrangements. While, clustering establishes an important part of professed data mining, a procedure of exploring and analyzing large volumes of data in order to determine valuable information. Outliers are points that do not conform with the common performance of the data. Therefore, we applied the K-Means and K-Medians clustering algorithms to calculate the run time complexity analysis in identification of outliers in clustering analysis. The result shows that the K-Medians clustering algorithm with the use of medians served as robust for its faster and effective run time facilities better performance compared to the K-Means clustering algorithm in detecting outliers.
Food image analysis: The big data problem you can eat!

Six of the ten leading causes of death in the United States can be directly linked to diet. Measuring accurate dietary intake, the process of determining what someone eats is considered to be an open research problem in the nutrition and health fields. We are developing image-based tools in order to automatically obtain accurate estimates of what foods a user consumes. We have developed a novel food record application using the embedded camera in a mobile device. This paper describes the current status of food image analysis and overviews problems that still need to be addressed.
A Theory of Attention : Variations in the Associability of Stimuli with Reinforcement

According to theories of selective attention, learning about a stimulus depends on attending to that stimulus; this is represented in two-stage models by saying that subjects switch in analyzers as well as learning stimulusresponse associations. This assumption, however, is equally well represented in a formal model by the incorporation of a stimulus-specific learning-rate parameter, a, into the equations describing changes in the associative strength of stimuli. Theories of selective attention have also assumed (a) that subjects learn to attend to and ignore relevant and irrelevant stimuli (i.e., that a may increase or decrease depending on the correlation of a stimulus with reinforcement) and (b) that there is an inverse relationship between the probabilities of attending to different stimuli (i.e., that an increase in a to one stimulus is accompanied by a decrease in a to others). The first assumption is used to explain the phenomena of acquired distinctiveness and dimensional transfer, the second those of overshadowing and blocking. Although the first assumption is justified by the data, the second is not: Overshadowing and blocking are better explained by the choice of an appropriate rule for changing a, such that a decreases to stimuli that signal no change from the probability of reinforcement predicted by other stimuli.
Transfer Deep Reinforcement Learning in 3 D Environments : An Empirical Study

The ability to transfer knowledge from previous experiences is critical for an agent to rapidly adapt to different environments and effectively learn new tasks. In this paper we conduct an empirical study of Deep Q-Networks (DQNs) where the agent is evaluated on previously unseen environments. We show that we can train a robust network for navigation in 3D environments and demonstrate its effectiveness in generalizing to unknown maps with unknown background textures. We further investigate the effectiveness of pretraining and finetuning for transferring knowledge between various scenarios in 3D environments. In particular, we show that the features learnt by the navigation network can be effectively utilized to transfer knowledge between a diverse set of tasks, such as object collection, deathmatch, and self-localization.
A Survey of Wearable Devices and Challenges

As smartphone penetration saturates, we are witnessing a new trend in personal mobile devices—wearable mobile devices or simply wearables as it is often called. Wearables come in many different forms and flavors targeting different accessories and clothing that people wear. Although small in size, they are often expected to continuously sense, collect, and upload various physiological data to improve quality of life. These requirements put significant demand on improving communication security and reducing power consumption of the system, fueling new research in these areas. In this paper, we first provide a comprehensive survey and classification of commercially available wearables and research prototypes. We then examine the communication security issues facing the popular wearables followed by a survey of solutions studied in the literature. We also categorize and explain the techniques for improving the power efficiency of wearables. Next, we survey the research literature in wearable computing. We conclude with future directions in wearable market and research.
Data integration flows for business intelligence

Business Intelligence (BI) refers to technologies, tools, and practices for collecting, integrating, analyzing, and presenting large volumes of information to enable better decision making. Today's BI architecture typically consists of a data warehouse (or one or more data marts), which consolidates data from several operational databases, and serves a variety of front-end querying, reporting, and analytic tools. The back-end of the architecture is a data integration pipeline for populating the data warehouse by extracting data from distributed and usually heterogeneous operational sources; cleansing, integrating and transforming the data; and loading it into the data warehouse. Since BI systems have been used primarily for off-line, strategic decision making, the traditional data integration pipeline is a oneway, batch process, usually implemented by extract-transform-load (ETL) tools. The design and implementation of the ETL pipeline is largely a labor-intensive activity, and typically consumes a large fraction of the effort in data warehousing projects. Increasingly, as enterprises become more automated, data-driven, and real-time, the BI architecture is evolving to support operational decision making. This imposes additional requirements and tradeoffs, resulting in even more complexity in the design of data integration flows. These include reducing the latency so that near real-time data can be delivered to the data warehouse, extracting information from a wider variety of data sources, extending the rigidly serial ETL pipeline to more general data flows, and considering alternative physical implementations. We describe the requirements for data integration flows in this next generation of operational BI system, the limitations of current technologies, the research challenges in meeting these requirements, and a framework for addressing these challenges. The goal is to facilitate the design and implementation of optimal flows to meet business requirements.
Towards interactive robots in autism therapy

This article discusses the potential of using interactive environments in autism therapy. We specifically address issues relevant to the Aurora project, which studies the possible role of autonomous, mobile robots as therapeutic tools for children with autism. Theories of mindreading, social cognition and imitation that informed the Aurora project are discussed and their relevance to the project is outlined. Our approach is put in the broader context of socially intelligent agents and interactive environments. We summarise results from trials with a particular mobile robot. Finally, we draw some comparisons to research on interactive virtual environments in the context of autism therapy and education. We conclude by discussing future directions and open issues.
Modeling Land-Use and LandCover Change 1 MODELING LAND-USE AND LANDCOVER CHANGE

Models are used in a variety of fields, including land change science, to better understand the dynamics of systems, to develop hypotheses that can be tested empirically, and to make predictions and/or evaluate scenarios for use in assessment activities. Modeling is an important component of each of the three foci outlined in the science plan of the Land-use and -cover change (LUCC) project (Turner et al. 1995) of the International Geosphere-Biosphere Program (IGBP) and the International Human Dimensions Program (IHDP). In Focus 1, on comparative land-use dynamics, models are used to help improve our understanding of the dynamics of land-use that arise from human decision-making at all levels, households to nations. These models are supported by surveys and interviews of decision makers. Focus 2 emphasizes development of empirical diagnostic models based on aerial and satellite observations of spatial and temporal land-cover dynamics. Finally, Focus 3 focuses specifically on the development of models of land-use and -cover change (LUCC) that can be used for prediction and scenario generation in the context of integrative assessments of global change.
Traffic Engineering in SDN/OSPF Hybrid Network

Traffic engineering under OSPF routes along the shortest paths, which may cause network congestion. Software Defined Networking (SDN) is an emerging network architecture which exerts a separation between the control plane and the data plane. The SDN controller can centrally control the network state through modifying the flow tables maintained by routers. Network operators can flexibly split arbitrary flows to outgoing links through the deployment of the SDN. However, SDN has its own challenges of full deployment, which makes the full deployment of SDN difficult in the short term. In this paper, we explore the traffic engineering in a SDN/OSPF hybrid network. In our scenario, the OSPF weights and flow splitting ratio of the SDN nodes can both be changed. The controller can arbitrarily split the flows coming into the SDN nodes. The regular nodes still run OSPF. Our contribution is that we propose a novel algorithm called SOTE that can obtain a lower maximum link utilization. We reap a greater benefit compared with the results of the OSPF network and the SDN/OSPF hybrid network with fixed weight setting. We also find that when only 30% of the SDN nodes are deployed, we can obtain a near optimal performance.
Towards automatic tweet generation: A comparative study from the text summarization perspective in the journalism genre

In recent years, Twitter has become one of the most important microblogging services of the Web 2.0. Among the possible uses it allows, it can be employed for communicating and broadcasting information in real time. The goal of this research is to analyze the task of automatic tweet generation from a text summarization perspective in the context of the journalism genre. To achieve this, different state-of-the-art summarizers are selected and employed for producing multi-lingual tweets in two languages (English and Spanish). A wide experimental framework is proposed, comprising the creation of a new corpus, the generation of the automatic tweets, and their assessment through a quantitative and a qualitative evaluation, where informativeness, indicativeness and interest are key criteria that should be ensured in the proposed context. From the results obtained, it was observed that although the original tweets were considered as model tweets with respect to their informativeness, they were not among the most interesting ones from a human viewpoint. Therefore, relying only on these tweets may not be the ideal way to communicate news through Twitter, especially if a more personalized and catchy way of reporting news wants to be performed. In contrast, we showed that recent text summarization techniques may be more appropriate, reflecting a balance between indicativeness and interest, even if their content was different from the tweets delivered by the news providers.