nils-herrmann/clean_scirepeval huggingface dataset:
Protocol / s Verification in the context of IoT based on E Theorem Prover

We present in this short communication “E Theorem Prover and Verification of Smart Devices & Protocols Based on IoT Environments – A Novel Suggestion on Informatics Framework of Smart Devices involving Hardware,Firmware & Software Verification.” keywords: E Theorem Prover/Smart Devices/Hardware/Firmware/Software/IoT  Based on the information contained in the published literature it is very much inspirational to use novel methodologies to probe hardware/firmware/software in an innovative way.E Theorem Prover is an excellent high performance tool to design develop,implement and test novel methodologies.We wish to highlight the importance of E Theorem Prover in the context of “Smart Devices” and their testing.Since E Theorem Prover is developed in C it is useful to test and verify “Smart Devices”.Refs[1-9] “E is a high performance theorem prover for full first-order logic with equality.It is based on the equational superposition calculus and uses a purely equational paradigm. It has been integrated into other theorem provers and it has been among the best-placed systems in several theorem proving competitions. E is developed by Stephan Schulz, originally in the Automated Reasoning Group at TU Munich.” Source : https://en.wikipedia.org/wiki/E_theorem_prover Informatics Framework & Implementation : Figure I. Approximate Informatics Framework to probe Smart Devices/Protocols/IoT/Algorithms [Based on Refs[1-9] && Additional Information on Software Used] Interfacing with the concerned computing environments or testing methodologies will vary to some extent. Readers are advised to check the requirements before using them.This is an attempt to inspire others to use Theorem Provers in Verification. For Example : We would like to suggest “Heuristics Approach” in designing,implementing and testing of verification methodologies involving hardware/firmware/software/IoT concepts.For more information please refer to additional information links and the references mentioned in this paper.  ( in extending our Testing/Verification Framework Using Python based Environments ) Figure II. Approximate Informatics Framework based on Python /E Prover to implement “Complex Test Environment” for large scale projects.
Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues

Knowledge is a broad and abstract notion that has defined epistemological debate in western philosophy since the classical Greek era. In the past Richard Watson was the accepting senior editor for this paper.  Review articles survey, conceptualize, and synthesize prior MIS research and set directions for future research. For more details see http://www.misq.org/misreview/announce.html few years, however, there has been a growing interest in treating knowledge as a significant organizational resource. Consistent with the interest in organizational knowledge and knowledge management (KM), IS researchers have begun promoting a class of information systems, referred to as knowledge management systems (KMS). The objective of KMS is to support creation, transfer, and application of knowledge in organizations. Knowledge and knowledge management are complex and multi-faceted concepts. Thus, effective development and implementation of KMS requires a foundation in several rich
Applying the User Experience Questionnaire (UEQ) in Different Evaluation Scenarios

A good user experience is central for the success of interactive products. To improve products concerning these quality aspects it is thus also important to be able to measure user experience in an efficient and reliable way. But measuring user experience is not an end in itself. Several different questions can be the reason behind the wish to measure the user experience of a product quantitatively. We discuss several typical questions associated with the measurement of user experience and we show how these questions can be answered with a questionnaire with relatively low effort. In this paper the user experience questionnaire UEQ is used, but the general approach may be transferred to other questionnaires as well.
Machine Learning and Statistical Approaches for Big Data : Issues , Challenges and Research Directions

Today, as we are observing, massive sized and complex structured data is becoming available from variety of diverse sources, organizations are making attempt to utilize these plentiful resources for the purpose of enhance innovation, increase decisional and operational efficiency. Machine learning is a kind of artificial intelligence method to discover knowledge for making intelligent decisions. Big Data has vast impacts on scientific discoveries and value creation. This paper presents an extensive literature study and review of latest advances, developments and new methodologies in researches on machine learning for processing big data. We have discussed various types of data types, learning methods, vital issues in big data processing and application of machine learning approaches in big data. Finally, we have outlined some open problems in this domain and our further research aims and directions.
Query Expansion with ConceptNet and WordNet: An Intrinsic Comparison

This paper compares the utilization of ConceptNet and WordNet in query expansion. Spreading activation selects candidate terms for query expansion from these two resources. Three measures including discrimination ability, concept diversity, and retrieval performance are used for comparisons. The topics and document collections in the ad hoc track of TREC-6, TREC-7 and TREC-8 are adopted in the experiments. The results show that ConceptNet and WordNet are complementary. Queries expanded with WordNet have higher discrimination ability. In contrast, queries expanded with ConceptNet have higher concept diversity. The performance of queries expanded by selecting the candidate terms from ConceptNet and WordNet outperforms that of queries without expansion, and queries expanded with a single resource.
Deep-Learning Convolutional Neural Networks for scattered shrub detection with Google Earth Imagery

There is a growing demand for accurate high-resolution land cover maps in many fields, e.g., in land-use planning and biodiversity conservation. Developing such maps has been performed using Object-Based Image Analysis (OBIA) methods, which usually reach good accuracies, but require a high human supervision and the best configuration for one image can hardly be extrapolated to a different image. Recently, the deep learning Convolutional Neural Networks (CNNs) have shown outstanding results in object recognition in the field of computer vision. However, they have not been fully explored yet in land cover mapping for detecting species of high biodiversity conservation interest. This paper analyzes the potential of CNNs-based methods for plant species detection using free high-resolution Google Earth images and provides an objective comparison with the state-of-the-art OBIA-methods. We consider as case study the detection of Ziziphus lotus shrubs, which are protected as a priority habitat under the European Union Habitats Directive. According to our results, compared to OBIA-based methods, the proposed CNNbased detection model, in combination with data-augmentation, transfer learning and pre-processing, achieves higher performance with less human ∗Both authors have contributed equally to this work 1 ar X iv :1 70 6. 00 91 7v 1 [ cs .C V ] 3 J un 2 01 7 intervention and the knowledge it acquires in the first image can be transferred to other images, which makes the detection process very fast. The provided methodology can be systematically reproduced for other species detection.
Temporal Abstraction in Temporal-difference Networks

The quality of a knowledge representation directly influences an agent’s ability to interact with an environment. Temporal-di↵erence (TD) networks, a recently introduced knowledge representation framework, model a world with a set of action-conditional predictions about sensations. Some key characteristics of TD networks are that they: 1) relate knowledge to sensations, 2) allow the agent to make predictions about other predictions (compositionality) and 3) provide a means for abstraction. The focus of this thesis is connecting high-level concepts to data by abstracting over space and time. Spatial abstraction in TD networks helps with scaling issues by grouping situations with similar sets of predictions into abstract states. A set of experiments demonstrate the advantages of using the abstract states as a representation for reinforcement learning. Temporal abstraction is added to TD networks by extending the framework to predict arbitrarily distant future outcomes. This extension is based on the options framework, an approach to including temporal abstraction in reinforcement-learning algorithms. Including options in the TD-network framework brings about a challenging problem: learning about multiple options from a single stream of data (also known as o↵-policy learning). The first algorithm for the o↵-policy learning of predictions about option outcomes is introduced in this thesis. Acknowledgements While only my name appears on the cover of this thesis, I could not have written it without the help of many others. Thanks to Dr. Rich Sutton, my supervisor, who introduced me to the research found in this thesis and who gave me a new perspective on the idea of artificial intelligence. I would also like to thank Dr. Michael Bowling and Dr. Petr Musilek for participating on my thesis committee. Thanks go to Dr. Mark Ring, who took the time to thoroughly proofread the initial draft of this thesis. Many thanks go to Brian Tanner, Anna Koop, Cosmin Paduraru and the rest of the RLAI group who provided help and friendship over the past two years (and who also managed to tolerate me for two years). And finally, I would like to thank my parents, Eddie and Evangeline Rafols, for all their encouragement and support. This thesis is a reflection of the love for learning you instilled in me.
A survey of autonomous landing techniques for UAVs

Landing an aerial vehicle is a very challenging problem. Pilots spend numerous hours practicing touchdowns because of the risk involved during landing phase. Developing autonomous landing technologies have been an active area of research over the past decade. This paper presents a review of landing techniques ranging from GPS based landing to vision based landing techniques; from basic nonlinear to intelligent, hybrid and robust control. It is aimed at providing a broad perspective on the status of the landing control problem and controller design. The paper provides a comparison based on parameters such as type of the vehicle, assumptions made in the problem design, techniques used and efficacy of the algorithm in real world conditions.
Neural Factorization Machines for Sparse Predictive Analytics

Many predictive tasks of web applications need to model categorical variables, such as user IDs and demographics like genders and occupations. To apply standard machine learning techniques, these categorical predictors are always converted to a set of binary features via one-hot encoding, making the resultant feature vector highly sparse. To learn from such sparse data effectively, it is crucial to account for the interactions between features.
 Factorization Machines (FMs) are a popular solution for efficiently using the second-order feature interactions. However, FM models feature interactions in a linear way, which can be insufficient for capturing the non-linear and complex inherent structure of real-world data. While deep neural networks have recently been applied to learn non-linear feature interactions in industry, such as the Wide&Deep by Google and DeepCross by Microsoft, the deep structure meanwhile makes them difficult to train.
 In this paper, we propose a novel model Neural Factorization Machine (NFM) for prediction under sparse settings. NFM seamlessly combines the linearity of FM in modelling second-order feature interactions and the non-linearity of neural network in modelling higher-order feature interactions. Conceptually, NFM is more expressive than FM since FM can be seen as a special case of NFM without hidden layers. Empirical results on two regression tasks show that with one hidden layer only, NFM significantly outperforms FM with a 7.3% relative improvement. Compared to the recent deep learning methods Wide&Deep and DeepCross, our NFM uses a shallower structure but offers better performance, being much easier to train and tune in practice.
Assessing sentence scoring techniques for extractive text summarization

0957-4174/$ see front matter 2013  http://dx.doi.org/10.1016/j.eswa.2013.04.023 ⇑ Corresponding author.  E-mail addresses: rflm@cin.ufpe.br (Rafael Ferreira), lscabral@gmail.com (L. de Souza Cabral), rdl@cin.ufpe.br (R.D. Lins), gfps.cin@gmail.com (G. Pereira e Silva), fred@cin.ufpe.br (F. Freitas), gdcc@cin.ufpe.br (G.D.C. , luciano.favaro@hp.com (L. Favaro). Rafael Ferreira a,⇑, Luciano de Souza Cabral , Rafael Dueire Lins , Gabriel Pereira e Silva , Fred Freitas , George D.C. Cavalcanti , Rinaldo Lima , Steven J. Simske , Luciano Favaro c
Stress prompts habit behavior in humans.

Instrumental behavior can be controlled by goal-directed action-outcome and habitual stimulus-response processes that are supported by anatomically distinct brain systems. Based on previous findings showing that stress modulates the interaction of "cognitive" and "habit" memory systems, we asked in the presented study whether stress may coordinate goal-directed and habit processes in instrumental learning. For this purpose, participants were exposed to stress (socially evaluated cold pressor test) or a control condition before they were trained to perform two instrumental actions that were associated with two distinct food outcomes. After training, one of these food outcomes was selectively devalued as subjects were saturated with that food. Next, subjects were presented the two instrumental actions in extinction. Stress before training in the instrumental task rendered participants' behavior insensitive to the change in the value of the food outcomes, that is stress led to habit performance. Moreover, stress reduced subjects' explicit knowledge of the action-outcome contingencies. These results demonstrate for the first time that stress promotes habits at the expense of goal-directed performance in humans.
Neural Tree Indexers for Text Understanding

Recurrent neural networks (RNNs) process input text sequentially and model the conditional transition between word tokens. In contrast, the advantages of recursive networks include that they explicitly model the compositionality and the recursive structure of natural language. However, the current recursive architecture is limited by its dependence on syntactic tree. In this paper, we introduce a robust syntactic parsing-independent tree structured model, Neural Tree Indexers (NTI) that provides a middle ground between the sequential RNNs and the syntactic tree-based recursive models. NTI constructs a full n-ary tree by processing the input text with its node function in a bottom-up fashion. Attention mechanism can then be applied to both structure and node function. We implemented and evaluated a binary-tree model of NTI, showing the model achieved the state-of-the-art performance on three different NLP tasks: natural language inference, answer sentence selection, and sentence classification, outperforming state-of-the-art recurrent and recursive neural networks .
Pretreatment technologies for an efficient bioethanol production process based on enzymatic hydrolysis: A review.

Biofuel produced from lignocellulosic materials, so-called second generation bioethanol shows energetic, economic and environmental advantages in comparison to bioethanol from starch or sugar. However, physical and chemical barriers caused by the close association of the main components of lignocellulosic biomass, hinder the hydrolysis of cellulose and hemicellulose to fermentable sugars. The main goal of pretreatment is to increase the enzyme accessibility improving digestibility of cellulose. Each pretreatment has a specific effect on the cellulose, hemicellulose and lignin fraction thus, different pretreatment methods and conditions should be chosen according to the process configuration selected for the subsequent hydrolysis and fermentation steps. This paper reviews the most interesting technologies for ethanol production from lignocellulose and it points out several key properties that should be targeted for low-cost and advanced pretreatment processes.
Real-Time Action Recognition with Enhanced Motion Vector CNNs

The deep two-stream architecture  exhibited excellent performance on video based action recognition. The most computationally expensive step in this approach comes from the calculation of optical flow which prevents it to be real-time. This paper accelerates this architecture by replacing optical flow with motion vector which can be obtained directly from compressed videos without extra calculation. However, motion vector lacks fine structures, and contains noisy and inaccurate motion patterns, leading to the evident degradation of recognition performance. Our key insight for relieving this problem is that optical flow and motion vector are inherent correlated. Transferring the knowledge learned with optical flow CNN to motion vector CNN can significantly boost the performance of the latter. Specifically, we introduce three strategies for this, initialization transfer, supervision transfer and their combination. Experimental results show that our method achieves comparable recognition performance to the state-of-the-art, while our method can process 390.7 frames per second, which is 27 times faster than the original two-stream method.
Report on Post-Quantum Cryptography

In recent years, there has been a substantial amount of research on quantum computers – machines that exploit quantum mechanical phenomena to solve mathematical problems that are difficult or intractable for conventional computers. If large-scale quantum computers are ever built, they will be able to break many of the public-key cryptosystems currently in use. This would seriously compromise the confidentiality and integrity of digital communications on the Internet and elsewhere. The goal of post-quantum cryptography (also called quantum-resistant cryptography) is to develop cryptographic systems that are secure against both quantum and classical computers, and can interoperate with existing communications protocols and networks. This Internal Report shares the National Institute of Standards and Technology (NIST)’s current understanding about the status of quantum computing and post-quantum cryptography, and outlines NIST’s initial plan to move forward in this space. The report also recognizes the challenge of moving to new cryptographic infrastructures and therefore emphasizes the need for agencies to focus on crypto agility.
Is dyslexia necessarily associated with negative feelings of self-worth? A review and implications for future research.

This paper will provide an overview of research in the past 20 years into the relationship between dyslexia and various aspects of self-perception, including self-concept, self-esteem, self-efficacy and locus of control. Problems are identified relating to the measurement of some of the most widely used constructs, as is the need for more precise identification of key variables relating to them. Critical issues concerning the assumptions that can be drawn from largely correlational data are highlighted also. The conclusion is drawn that an alternative approach to research in this area is required to enhance our understanding of how those suffering from learning disabilities of a dyslexic nature develop a positive or negative sense of identity. Suggestions are made as to how this can be achieved by drawing upon attribution theory and other aspects of social psychology.
DLAU: A Scalable Deep Learning Accelerator Unit on FPGA

As the emerging field of machine learning, deep learning shows excellent ability in solving complex learning problems. However, the size of the networks becomes increasingly large scale due to the demands of the practical applications, which poses significant challenge to construct a high performance implementations of deep learning neural networks. In order to improve the performance as well as to maintain the low power cost, in this paper we design deep learning accelerator unit (DLAU), which is a scalable accelerator architecture for large-scale deep learning networks using field-programmable gate array (FPGA) as the hardware prototype. The DLAU accelerator employs three pipelined processing units to improve the throughput and utilizes tile techniques to explore locality for deep learning applications. Experimental results on the state-of-the-art Xilinx FPGA board demonstrate that the DLAU accelerator is able to achieve up to  $36.1 {\times }$  speedup comparing to the Intel Core2 processors, with the power consumption at 234 mW.
Urban sprawl : metrics , dynamics and modelling using GIS

Urban sprawl refers to the extent of urbanisation, which is a global phenomenon mainly driven by population growth and large scale migration. In developing countries like India, where the population is over one billion, one-sixth of the worldâ€TMs population, urban sprawl is taking its toll on the natural resources at an alarming pace. Urban planners require information related to the rate of growth, pattern and extent of sprawl to provide basic amenities such as water, sanitation, electricity, etc. In the absence of such information, most of the sprawl areas lack basic infrastructure facilities. Pattern and extent of sprawl could be modelled with the help of spatial and temporal data. GIS and remote sensing data along with collateral data help in analysing the growth, pattern and extent of sprawl. With the spatial and temporal analyses along with modelling it was possible to identify the pattern of sprawl and subsequently predict the nature of future sprawl. This paper brings out the extent of sprawl taking place over a period of nearly three decades using GIS and Remote Sensing. The study also attempts to describe a b Purchase Export Previous article Next article Check if you have access through your login credentials or your institution.
Trajectory design and control for aggressive formation flight with quadrotors

In this work we consider the problem of controlling a team of microaerial vehicles moving quickly through a three-dimensional environment while maintaining a tight formation. The formation is specified by a shape matrix that prescribes the relative separations and bearings between the robots. Each robot plans its trajectory independently based on its local information of other robot plans and estimates of states of other robots in the team to maintain the desired shape. We explore the interaction between nonlinear decentralized controllers, the fourth-order dynamics of the individual robots, the time delays in the network, and the effects of communication failures on system performance. An experimental evaluation of our approach on a team of quadrotors suggests that suitable performance is maintained as the formation motions become increasingly aggressive and as communication degrades.