nils-herrmann/clean_scirepeval huggingface dataset:
Applying an online game based formative assessment in a flowchart based intelligent tutoring system for improving problem solving skills
Venue: Comput. Educ.

Intelligent Tutoring Systems (ITSs) have been producing consistent learning gains for decades. However, a common problem with these systems is maintaining student engagement without reducing the learning benefits. In order to balance the learning benefits of ITSs with the motivational benefits of games, an online formative assessment game called tic tac toe quiz for single player (TRIS Q SP) is incorporated in a Flowchart based Intelligent Tutoring System (FITS) benefiting from Bayesian networks for the process of decision making, for learning computer programming. This assessment game combines tic tac toe with online assessment, and revises the rule of tic tac toe for stimulating students to use online formative assessment actively. Finally, an empirical investigation carried out to evaluate the performance of FITS indicating that considerable gains for the experimental group over the control group. The proposed system extensively enhanced students' learning interest, attitude and degree of technology acceptance, as well as improved their achievements in problem solving activities. Additionally, the findings also reveal that providing Immediate Elaborated Feedback (IEF) for each answered question in TRIS Q SP is the optimal design, as it facilitated the enhancement of programming knowledge acquisition when comparing it with the No Immediate Elaborated Feedback (NIEF) condition. We model a game based formative assessment intelligent tutoring system.The proposed system supports skills acquisition in computer programming.The system can better facilitate problem solving skills acquisition.An experimental study was designed to investigate the effectiveness of the system.Result shows Immediate Elaborated Feedback (IEF) are better than No IEF.
CO2 capture via adsorption in amine functionalized sorbents

Amine functionalized solid adsorbents are promising materials for the capture of CO 2 because of their tunability and lower heat capacity compared to liquid amine solutions. This review focuses on recent advances in the use of solid supported amines for CO 2 capture, with emphasis on amine functionalized metal organic framework (MOF) materials. The recently reported diamine functionalized Mg DOBPDC MOF demonstrates intriguing performance for CO 2 capture, offering a unique adsorption mechanism with favorable energetics. Stable performance for CO 2 capture under humid conditions and easy regenerability of the adsorbent are critical to the realization of practical CO 2 adsorbents. Scalable regeneration methods such as temperature swing in a CO 2 purge stream and temperature swing via steam stripping are discussed. Emerging gas solid contacting strategies for CO 2 capture with amine adsorbents are also discussed.
Div Grad Curl And All That An Informal Text On Vector Calculus

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Performance of IR UWB cross layer ranging protocols under on body channel models with body area networks
Venue: Ann. des Telecommunications

This paper deals with the positioning performance of the 3 Way ranging protocol (3 WR) in a wireless body area network (WBAN) The purpose is to propose a new cooperative algorithm to improve the number of estimated positions with a conditional permutation of the on body anchors. To do so, we first evaluate and analyze the positioning success rate under a realistic mobility scenario and using two scheduling strategies: Broadcast single node localization (P2P B) and aggregated and broadcast (A&B) with a medium access control (MAC) layer based on time division multiple access (TDMA) The 3 WR estimates the distance between two nodes placed on the body with the transmission of three packets. The wireless transmission is based on impulse radio ultra wideband (IR UWB) However, these transactions can be lost through the WBAN channel leading into a "bad positioning service." We consider a physical layer based on IR UWB with three different channels: (1) an empirical theoretical model based on the on body CM3 path loss channel (Anechoic chamber) (2) a simulated channel calculated by ray tracing with the PyLayers simulator, and (3) an experimental channel model obtained by measurements. Our results show that the channel affects the positioning success rate that decreases as a function of the sensitivity threshold at the receiver. This can be solved with long and short term analysis for the choice of virtual anchors to increase the positioning performance.
Poverty reduction through women's entrepreneurial activities in rural Andhra Pradesh, India

In India, ensuring sustainable livelihoods and alleviating rural poverty remain serious challenges for policy makers. There are many problems including a lack of access to institutional/farm credit, lack of innovative approach for agricultural development, steep increases in the prices of fertilizer, high indebtedness, and imperfect market conditions. The lack of institutional support often traps the poor into a cycle of perpetual debt and poverty, and in extreme conditions, they may not even have access to food. In these circumstances, ensuring livelihood security for the poor through the promotion of various small scale entrepreneurial activities has received critical attention. Rural banks have been entrusted with the responsibility to provide adequate and affordable credits to rural people to improve their livelihood opportunities. Self Help Groups (SHGs) have become instrumental in delivering tiny loans to the poor women through banks to promote various entrepreneurial activities. Particularly in Andhra Pradesh, a southern state, the SHG movement has been playing a pivotal role by bringing a radical change in the position of women from daily wage laborer to self employed entrepreneur. As an empirical analysis, this paper critically examines the potentials of SHGs to emerge as institutions of rural entrepreneurship, and of running various small scale business ventures by poor women in Andhra Pradesh. Besides, the paper attempts to highlight the major operational challenges in the functioning of SHGs.
Folk linguistics and language teaching education. A case study in an Italian secondary school

This paper, after shortly introducing Folk Linguistics by defining its domain of competence [cf. Preston, Dennis R. ed. 1999. Handbook of Perceptual Dialectology.  Mouton de Gruyter] attempts to draw an outline of the advantages that can be derived from its application to language teaching education from several viewpoints. In order to do so, similarly to what happens with sociolinguistics, we will at first suggest introducing a distinction between micro language teaching studies and macro language teaching studies. The former typology of studies is often the direct consequence of the latter, since it lays its foundations and indicates its lines and directions of development. This distinction serves to better define the relation between Folk Linguistics and language teaching education, as language attitudes, being a fundamental component of speakers' language identity [Berruto, Gaetano. 2005. Fondamenti di sociolinguistica. Roma Bari: Laterza] and their will and willingness to learn a new language, have an immediate and essential impact especially on the macro perspective. Different possible areas of investigation will then be singled out and described in detail, with reference to the advantages of their application in language teaching education planning and practice. Finally, a case study relating to an Italian Secondary School will be illustrated.
Mobile and ubiquitous learning in higher education settings. A systematic review of empirical studies
Venue: Comput. Hum. Behav.

Mobile and ubiquitous learning are increasingly attracting academic and public interest, especially in relation to their application in higher education settings.The systematic analysis of 36 empirical papers supports the view that knowledge gains from instructionist learning designs are facilitated by distributed and more frequent learning activities enabled by push mechanisms. They also lend themselves to the activation of learners during classroom lectures. In addition, and as a particular advantage of mobile technology, "hybrid" designs, where learners create multimodal representations outside the classroom and then discussed their substantiated experiences with peers and educators, helped to connect learning in formal and more informal and personalized learning environments.Generally, empirical evidence that would favour the broad application of mobile and ubiquitous learning in higher education settings is limited and because mobile learning projects predominantly take instructionist approaches, they are non transformatory in nature. However, by harnessing the increasing access to digital mobile media, a number of unprecedented educational affordances can be operationalised to enrich and extend more traditional forms of higher education. Systematic review to study mobile and ubiquitous learning in higher education.Positive outcomes associated mainly with instructionist and hybrid designs.Instructionist benefits caused by more frequent learning activities.Hybridisation links formal education with informal and personalized learning.Limited evidence to legitimize broad application of mobile learning in HE.
Slow wave sleep and accelerated forgetting
Venue: Cortex

We investigated whether the benefit of slow wave sleep (SWS) for memory consolidation typically observed in healthy individuals is disrupted in people with accelerated long term forgetting (ALF) due to epilepsy. SWS is thought to play an active role in declarative memory in healthy individuals and, furthermore, electrographic epileptiform activity is often more prevalent during SWS than during wakefulness or other sleep stages. We studied the relationship between SWS and the benefit of sleep for memory retention using a word pair associates task. In both the ALF and the healthy control groups, sleep conferred a memory benefit. However, the relationship between the amount of SWS and sleep related memory benefits differed significantly between the groups. In healthy participants, the amount of SWS correlated positively with sleep related memory benefits. In stark contrast, the more SWS, the smaller the sleep related memory benefit in the ALF group. Therefore, contrary to its role in healthy people, SWS associated brain activity appears to be deleterious for memory in patients with ALF.
Radical innovation in scaling up: Boeing's Dreamliner and the challenge of socio technical transitions

Radical technological innovations are needed to achieve sustainability, but such innovations confront unusually high barriers, as they often require sociotechnical transitions. Here we use the theoretical perspectives and methods of Science and Technology Studies (STS) to demonstrate ways that existing theories of innovation and sociotechnical transitions, such as the Multi Level Perspective (MLP) can be expanded. We test the MLP by applying STS methods and concepts to analyze the history of aircraft composites (lightweight materials that can reduce fuel consumption and greenhouse gas emissions) and use this case to develop a better understanding of barriers to radical innovation. In the MLP, "radical innovation" occurs in local niches protected spaces for experimentation and is then selected by a sociotechnical regime. The history of composite materials demonstrates that radical innovation could not be confined to "niches," but that the process of scaling up to a wholly new product itself required radical innovation in composites. Scaling up a process innovation to make a new product itself required radical innovation. These findings suggest a need to refine sociotechnical transitions theories to account for technologies that require radical innovation in the process of scaling up from the level of sociotechnical niche to regime.
DSSM with text hashing technique for text document retrieval in next generation search engine for big data and data analytics
Venue: 2016 IEEE International Conference on Engineering and Technology (ICETECH)

Digital world is coming, were data as become big data with ever increase in large volume of digital information available in terms of text documents. This tends for data extraction, enrichment, analysis and retrieval of text documents which are in the form of unstructured nature becomes a major problem in search engine. Traditionally text documents are the source of storing our information; either personal or professional. Today text documents are generating at very high speed, and need to be process the data on time to upgrade the search engine. It is also important for organizations including private and public which have been collecting large volume of domain specific text document information, which may contain national intelligence, education, medical information, business and marketing. In this paper we present a system that enriches the information retrieval process of text documents in search engine from unstructured data and bringing the big data and data analytics world into educational sector and make the best of both worlds by using the latest cutting edge technology deep structured semantic modeling with text hashing and proposing a next generation search engine.
Holographic Embeddings of Knowledge Graphs
Venue: AAAI

Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs. In this work, we propose holographic embeddings (HOLE) to learn compositional vector space representations of entire knowledge graphs. The proposed method is related to holographic models of associative memory in that it employs circular correlation to create compositional representations. By using correlation as the compositional operator, HOLE can capture rich interactions but simultaneously remains efficient to compute, easy to train, and scalable to very large datasets. Experimentally, we show that holographic embeddings are able to outperform state of the art methods for link prediction on knowledge graphs and relational learning benchmark datasets.
A Review of Relational Machine Learning for Knowledge Graphs
Venue: Proceedings of the IEEE

Relational machine learning studies methods for the statistical analysis of relational, or graph structured, data. In this paper, we provide a review of how such statistical models can be "trained" on large knowledge graphs, and then used to predict new facts about the world (which is equivalent to predicting new edges in the graph) In particular, we discuss two fundamentally different kinds of statistical relational models, both of which can scale to massive data sets. The first is based on latent feature models such as tensor factorization and multiway neural networks. The second is based on mining observable patterns in the graph. We also show how to combine these latent and observable models to get improved modeling power at decreased computational cost. Finally, we discuss how such statistical models of graphs can be combined with text based information extraction methods for automatically constructing knowledge graphs from the Web. To this end, we also discuss Google's knowledge vault project as an example of such combination.
Formal Verification of Smart Contracts: Short Paper
Venue: PLAS@CCS

Ethereum is a framework for cryptocurrencies which uses blockchain technology to provide an open global computing platform, called the Ethereum Virtual Machine (EVM) EVM executes bytecode on a simple stack machine. Programmers do not usually write EVM code; instead, they can program in a JavaScript like language, called Solidity, that compiles to bytecode. Since the main purpose of EVM is to execute smart contracts that manage and transfer digital assets (called Ether) security is of paramount importance. However, writing secure smart contracts can be extremely difficult: due to the openness of Ethereum, both programs and pseudonymous users can call into the public methods of other programs, leading to potentially dangerous compositions of trusted and untrusted code. This risk was recently illustrated by an attack on TheDAO contract that exploited subtle details of the EVM semantics to transfer roughly $50M worth of Ether into the control of an attacker. In this paper, we outline a framework to analyze and verify both the runtime safety and the functional correctness of Ethereum contracts by translation to F* a functional programming language aimed at program verification.
Formal Verification of Smart Contracts

Ethereum is a cryptocurrency framework that uses blockchain technology to provide an open distributed computing platform, called the Ethereum Virtual Machine (EVM) EVM programs are written in bytecode which operates on a simple stack machine. Programmers do not usually write EVM code; instead, they can program in a JavaScript like language called Solidity that compiles to bytecode. Since the main application of EVM programs is as smart contracts that manage and transfer digital assets, security is of paramount importance. However, writing trustworthy smart contracts can be extremely difficult due to the intricate semantics of EVM and its openness: both programs and pseudonymous users can call into the public methods of other programs. This problem is best illustrated by the recent attack on TheDAO contract, which allowed roughly $50M USD worth of Ether to be transferred into the control of an attacker. Recovering the funds required a hard fork of the blockchain, contrary to the code is law premise of the system. In this paper, we outline a framework to analyze and verify both the runtime safety and the functional correctness of Solidity contracts in F, a functional programming language aimed at program verification.
Hormonal Replacement in Hypopituitarism in Adults: An Endocrine Society Clinical Practice Guideline.
Venue: The Journal of clinical endocrinology and metabolism

To formulate clinical practice guidelines for hormonal replacement in hypopituitarism in adults.  The participants include an Endocrine Society appointed Task Force of six experts, a methodologist, and a medical writer.   Using an evidence based approach, this guideline addresses important clinical issues regarding the evaluation and management of hypopituitarism in adults, including appropriate biochemical assessments, specific therapeutic decisions to decrease the risk of co morbidities due to hormonal over replacement or under replacement, and managing hypopituitarism during pregnancy, pituitary surgery, and other types of surgeries.
Understandings and perceptions of spirituality held by multidisciplinary professionals involved in a community based palliative care organization: Implications for professional practice.

As a universal human experience, spirituality is innate to humanity. Nevertheless, the attempt to define spirituality within health care has led to a range of diverse and often nebulous definitions. Holistic practice within palliative care includes the physical, psychological, social, and spiritual aspects of human beings, all of which can facilitate pain for the terminally ill. Commonly, spiritual pain is inadequately addressed within palliative care. This is due to spirituality not being identified or performed by multidisciplinary professionals, who feel untrained and inadequate for the task due to a lack of education or professional development in the area of spirituality. As a part of holistic practice these professionals are called to provide basic spiritual care and make timely spiritual referrals within their everyday practice. A lexicon of spirituality was required to bring appropriate language for the identification and provision of basic spiritual care and judicious referrals to spiritual professionals. As more terminally ill people indicate a desire to die at home, true holistic palliative care that is community based is becoming crucial. For this research study a small community based palliative care organization, Ballarat Hospice Care Incorporated, was chosen to be the case study. Understandings and perceptions of spirituality and spiritual care were developed and articulated through extended focus groups and semi structured individual interviews with members of the multidisciplinary team. The employment of a hermeneutic phenomenological theoretical perspective allowed the essence of the experience of spirituality at BHCI to emerge and be given language by those who worked there. van Manen's four fundamental lifeworld existentials of lived space, lived time, lived other, and lived body were used as guides to reflection and interpretation of the textual conversations. Consequently, the definitional framework applied in this research study was of spirituality as connectedness with Self, with Other, with the world, and with mystery/transcendence, not static, but constantly evolving towards ultimate unity. Correspondingly, it also was useful to apply the concept of disconnectedness which identified experiences of possible spiritual pain and despair. This spirituality schema brings common language to what had previously been seen as an ill defined concept. The connectedness and disconnectedness framework is accessible and can be practically employed to assist the multidisciplinary team in defining and identifying spirituality and spiritual care. Importantly, it is inclusive of all people regardless of belief, values, and personal meanings.
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Venue: ICLR

In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications. Comparatively, unsupervised learning with CNNs has received less attention. In this work we hope to help bridge the gap between the success of CNNs for supervised learning and unsupervised learning. We introduce a class of CNNs called deep convolutional generative adversarial networks (DCGANs) that have certain architectural constraints, and demonstrate that they are a strong candidate for unsupervised learning. Training on various image datasets, we show convincing evidence that our deep convolutional adversarial pair learns a hierarchy of representations from object parts to scenes in both the generator and discriminator. Additionally, we use the learned features for novel tasks demonstrating their applicability as general image representations.