nils-herrmann/clean_scirepeval huggingface dataset:
Plasma : Scalable Autonomous Smart Contracts

Plasma is a proposed framework for incentivized and enforced execution of smart contracts which is scalable to a significant amount of state updates per second (potentially billions) enabling the blockchain to be able to represent a significant amount of decentralized financial applications worldwide. These smart contracts are incentivized to continue operation autonomously via network transaction fees, which is ultimately reliant upon the underlying blockchain (e.g. Ethereum) to enforce transactional state transitions. We propose a method for decentralized autonomous applications to scale to process not only financial activity, but also construct economic incentives for globally persistent data services, which may produce an alternative to centralized server farms. Plasma is composed of two key parts of the design: Reframing all blockchain computation into a set of MapReduce functions, and an optional method to do Proof-of-Stake token bonding on top of existing blockchains with the understanding that the Nakamoto Consensus incentives discourage block withholding. This construction is achieved by composing smart contracts on the main blockchain using fraud proofs whereby state transitions can be enforced on a parent blockchain. We compose blockchains into a tree hierarchy, and treat each as an individual branch blockchain with enforced blockchain history and MapReducible computation committed into merkle proofs. By framing one’s ledger entry into a child blockchain which is enforced by the parent chain, one can enable incredible scale with minimized trust (presuming root blockchain availability and correctness). The greatest complexity around global enforcement of non-global data revolves around data availability and block withholding attacks, Plasma has mitigations for this issue by allowing for exiting faulty chains while also creating mechanisms to incentivize and enforce continued correct execution of data. As only merkleized commitments are broadcast periodically to the root blockchain (i.e. Ethereum) during non-faulty states, this can allow for incredibly scalable, low cost transactions and computation. Plasma enables persistently operating decentralized applications at high scale.
Data Driven Governing Equations Approximation Using Deep Neural Networks

We present a numerical framework for approximating unknown governing equations using observation data and deep neural networks (DNN). In particular, we propose to use residual network (ResNet) as the basic building block for equation approximation. We demonstrate that the ResNet block can be considered as a one-step method that is exact in temporal integration. We then present two multi-step methods, recurrent ResNet (RT-ResNet) method and recursive ReNet (RS-ResNet) method. The RT-ResNet is a multi-step method on uniform time steps, whereas the RS-ResNet is an adaptive multi-step method using variable time steps. All three methods presented here are based on integral form of the underlying dynamical system. As a result, they do not require time derivative data for equation recovery and can cope with relatively coarsely distributed trajectory data. Several numerical examples are presented to demonstrate the performance of the methods.
Multi-Objective Decision Making

Many real-world tasks require making decisions that involve multiple possibly conflicting objectives. To succeed in such tasks, intelligent systems need planning or learning algorithms that can e ciently find di↵erent ways of balancing the trade-o↵s that such objectives present. In this tutorial, we provide an introduction to decision-theoretic approaches to coping with multiple objectives. We first present an overview of multi-objective decision problems, with real-world examples. Then, we show that di↵erent assumptions about these problems lead to di↵erent solution concepts such as the convex hull and the Pareto front. Next, we provide an overview of state-of-the-art algorithms for tackling them, such as multi-objective variants of dynamic programming. Finally, we highlight some applications of multiobjective methods and discuss some of the most important open questions.
Analyses of heart rate variability in young soccer players: The effects of sport activity

The use of heart rate variability (HRV) in the management of sport training is a practice which tends to spread, especially in order to prevent the occurrence of states of fatigue.



To estimate the HRV parameters obtained using a heart rate recording, according to different loads of sporting activities, and to make the possible link with the appearance of fatigue.



Eight young football players, aged 14.6 years+/-2 months, playing at league level in Rhône-Alpes, training for 10 to 20 h per week, were followed over a period of 5 months, allowing to obtain 54 recordings of HRV in three different conditions: (i) after rest (ii) after a day with training and (iii) after a day with a competitive match.



Under the effect of a competitive match, the HRV temporal indicators (heart rate, RR interval, and pNN50) were significantly altered compared to the rest day. The analysis of the sympathovagal balance rose significantly as a result of the competitive constraint (0.72+/-0.17 vs. 0.90+/-0.20; p<0.05).



The main results obtained show that the HRV is an objective and non-invasive monitoring of management of the training of young sportsmen. HRV analysis allowed to highlight any neurovegetative adjustments according to the physical loads. Thus, under the effect of an increase of physical and psychological constraints that a football match represents, the LF/HF ratio rises significantly; reflecting increased sympathetic stimulation, which beyond certain limits could be relevant to prevent the emergence of a state of fatigue.
Factors and actors leading to the adoption of a JavaScript framework

The increasing popularity of JavaScript has led to a variety of JavaScript frameworks that aim to help developers to address programming tasks. However, the number of JavaScript frameworks has risen rapidly to thousands of versions. It is challenging for practitioners to identify the frameworks that best fit their needs and to develop new ones which fit such needs. Furthermore, there is a lack of knowledge regarding what drives developers toward the choice. This paper explores the factors and actors that lead to the choice of a JavaScript framework. We conducted a qualitative interpretive study of semi-structured interviews. We interviewed 18 decision makers regarding the JavaScript framework selection, up to reaching theoretical saturation. Through coding of the interview responses, we offer a model of desirable JavaScript framework adoption factors. The factors are grouped into categories that are derived via the Unified Theory of Acceptance and Use of Technology. The factors are performance expectancy (performance, size), effort expectancy (automatization, learnability, complexity, understandability), social influence (competitor analysis, collegial advice, community size, community responsiveness), facilitating conditions (suitability, updates, modularity, isolation, extensibility), and price value. A combination of four actors, which are customer, developer, team, and team leader, leads to the choice. Our model contributes to the body of knowledge related to the adoption of technology by software engineers. As a practical implication, our model is useful for decision makers when evaluating JavaScript frameworks, as well as for developers for producing desirable frameworks.
Railway EMI impact on train operation and environment

Several studies in Sweden have looked into railway electromagnetic interference (EMI) either to discover the source of the interference or to determine if the equipment in the system is performing properly. The movement of rolling stock along an electrified track produces certain EMI events. Transient electromagnetic fields are produced in the signalling system when the train leaves the neutral section of the overhead power line and enters the powered section. These transient EM fields are mainly produced by the engine. The track's infrastructure system has been tested for EMI events, but this phenomenon affects the surrounding environment as well, up to at least 10 meters from the track. The infrastructure is designed so that the return current from locomotives should go through the running rails, but occasionally the ground acts as a conductor, transmitting current to areas that are distant from the rail. The paper reviews the status of Swedish railways with respect to electromagnetic compatibility. This TREND project is a joint project with 7 FP EU.
Incidence and virulence characteristics of Aeromonas spp. in fish

This study was conducted to evaluate the presence of Aeromonas spp. in raw and ready-to-eat (RTE) fish commonly consumed in Assiut city, Egypt, and to determine virulence factors due to they play a key role in their pathogenicity.



A total of 125 samples of raw and RTE fish samples were taken from different fish markets and fish restaurants in Assiut Governorate and screened for the presence of Aeromonas spp. by enrichment on tryptic soy broth then incubated at 30°C for 24 h. Plating unto the sterile Petri dishes containing Aeromonas agar base to which Aeromonas selective supplement was added. The plates were incubated at 37°C for 24 h. Presumptive Aeromonas colonies were biochemically confirmed and analyzed for pathogenicity by hemolysin production, protease, and lipase detection.



The results indicated that raw fish were contaminated with Aeromonas spp. (40% in wild and 36% in cultured Nile tilapia). Regarding RTE, Aeromonas spp. could be isolated with the percentage of 16%, 28% and 20% in fried Bolti, grilled Bolti and fried Bayad, respectively. Out of 35 isolates obtained, 22 were categorized as Aeromonas hydrophila, 12 were classified as Aeromonas sobria and Aeromonas caviae were found in only one isolate. The virulence factors of Aeromonas spp. were detected and the results showed that all isolates produced of hemolysin (91.4%), protease (77.1%), and lipase enzyme (17.1%).



This study indicates that the presence of A. hydrophila with virulence potential in fresh and RTE fish may be a major threat to public health.
Towards Materials for Computational Heirlooms: Blockchains and Wristwatches

This paper explores the contrasting notions of "permanance and disposability," "the digital and the physical," and "symbolism and function" in the context of interaction design. Drawing from diverse streams of knowledge, we describe a novel design direction for enduring computational heirlooms based on the marriage of decentralized, trustless software and durable mobile hardware. To justify this concept, we review prior research; attempt to redefine the notion of "material;" propose blockchain-based software as a particular digital material to serve as a substrate for computational heirlooms; and argue for the use of mobile artifacts, informed in terms of their materials and formgiving practices by mechanical wristwatches, as its physical embodiment and functional counterpart. This integration is meant to enable mobile and ubiquitous interactive systems for the storing, experiencing, and exchanging value throughout multiple human lifetimes; showcasing the feats of computational sciences and crafts; and enabling novel user experiences.
End-to-End Reinforcement Learning for Automatic Taxonomy Induction

§ Action (term-pair) Representation • Dependency Paths between x, y • Wx: Word Embedding of x •  Word Embedding of y • F(x, y): Surface (Ends with, Contains, etc.), Frequency (pattern-based co-occur info), and Generality (edge not too general or narrow) Features § Performance Study on End-to-End Taxonomy Induction: • WordNet (533/144/144 taxonomies for training, validation, and test set, size (10, 50], depth=4, animals, daily necessities, etc.) § Compared methods: • TAXI : pattern-based method that ranked 1st in the SemEval-2016 Task 13 competition • HypeNET : state-of-the-art hypernymy detection method • HypeNET + MST (maximum spanning tree): post-processing of HypeNET to prune the hypernym graph into a tree • Bansal et al. (2014) : state-of-the-art taxonomy induction method • SubSeq : state-of-the-art results on the SemEval-2016 Task 13 • Taxo-RL (RE, with virtual root embedding), Taxo-RL (NR, with new root addition), Taxo-RL (NR) + FG (with frequency and generality features) • Taxo-RL (partial, allows partial taxonomy), Taxo-RL (full, has to use all terms in the vocabulary)
Major Traits / Qualities of Leadership

We live in a world with a population of more than 7.1 Billion, have we ever imagine how many Leaders do we have? Yes, most of us are followers; we live in a world where we follow what have been commanded. The intension of this paper is to equip everyone with some knowledge to know how we can identify who leaders are, are you one of them, and how can we help our-selves and other develop leadership qualities. The Model highlights various traits which are very necessary for leadership. This paper have been investigate and put together after probing almost 30 other research papers. The Principal result we arrived on was that the major/ essential traits which are identified in a Leader are Honesty, Integrity, Drive (Achievement, Motivation, Ambition, Energy, Tenacity and Initiative), Self Confidence, Vision and Cognitive Ability. The Key finding also says that the people with such qualities are not necessary to be in politics, but they are from various walks of life such as major organization, different culture, background, education and ethnicities. Also we found out that just possessing of such traits alone does not guarantee one leadership success as evidence shows that effective leaders are different in nature from most of the other people in certain key respects. So, let us go through the paper to enhance out our mental abilities to search for the Leaders out there.
Prediction of time series by statistical learning: general losses and fast rates

We establish rates of convergences in statistical learning for time series forecasting. Using the PAC-Bayesian approach, slow rates of convergence √ d/n for the Gibbs estimator under the absolute loss were given in a previous work [7], where n is the sample size and d the dimension of the set of predictors. Under the same weak dependence conditions, we extend this result to any convex Lipschitz loss function. We also identify a condition on the parameter space that ensures similar rates for the classical penalized ERM procedure. We apply this method for quantile forecasting of the French GDP. Under additional conditions on the loss functions (satisfied by the quadratic loss function) and for uniformly mixing processes, we prove that the Gibbs estimator actually achieves fast rates of convergence d/n. We discuss the optimality of these different rates pointing out references to lower bounds when they are available. In particular, these results bring a generalization the results of [29] on sparse regression estimation to some autoregression.
Embodied spatial cognition: Biological and artificial systems

In this paper we sketch out a computational theory of spatial cognition motivated by navigational behaviours, ecological requirements, and neural mechanisms as identified in animals and man. Spatial cognition is considered in the context of a cognitive agent built around the action-perception cycle. Besides sensors and effectors, the agent comprises multiple memory structures including a working memory and a longterm memory stage. Spatial longterm memory is modeled along the graph approach, treating recognizable places or poses as nodes and navigational actions as links. Models of working memory and its interaction with reference memory are discussed. The model provides an overall framework of spatial cognition which can be adapted to model different levels of behavioural complexity as well as interactions between working and longterm memory. A number of design questions for building cognitive robots are derived from comparison with biological systems and discussed in the paper.
Secure multi-hop routing protocols in Wireless Sensor Networks: Requirements, challenges and solutions

Wireless Sensor Networks (WSNs) have been a subject of extensive research and have undergone explosive growth in the last few years. WSNs utilize collaborative measures such as data gathering, aggregation, processing, and management of sensing activities for enhanced performance. In order to communicate with the sink node, node having low power may have to traverse multi-hops. This requires neighbors' nodes to be used as relays. However, if the relay nodes are compromised or malicious, they may leak confidential information to unauthorized nodes in the WSN. Moreover, in many WSN applications, the deployment of sensor nodes is carried out in an ad-hoc fashion without careful examination. In such networks it is desirable to ensure the source to sink privacy and maximize the lifetime of the network, by finding secure energy-efficient route discovery and forwarding mechanisms. Careful management is also necessary, as processing required for secure routing is distributed over multiple nodes. An important consideration in this regard is energy-aware secure routing, which is significant in ensuring smooth operation of WSNs. As, these networks deal in sensitive data and are vulnerable to attack, it is important to make them secure against various types of threats. However, resource constraints could make the design, deployment and management of large WSNs a challenging proposition. The purpose of this paper is to highlight routing based security threats, provide a detailed assessment of existing solutions and present a Trust-based Energy Efficient Secure Routing Protocol (TEESR). The paper also highlights future research directions in of secure routing in multi-hop WSNs.
Security in Near Field Communication ( NFC ) Strengths and Weaknesses

This paper gives a comprehensive analysis of security with respect to NFC. It is not limited to a certain application of NFC, but it uses a systematic approach to analyze the various aspects of security whenever an NFC interface is used. The authors want to clear up many misconceptions about security and NFC in various applications. The paper lists the threats, which are applicable to NFC, and describes solutions to protect against these threats. All of this is given in the context of currently available NFC hardware, NFC applications and possible future developments of NFC.
Organizing Instruction and Study to Improve Student Learning. IES Practice Guide. NCER 2007-2004.

Disclaimer The opinions and positions expressed in this practice guide are the authors' and do not necessarily represent the opinions and positions of the Institute of Education Sciences or the U.S. Department of Education. This practice guide should be reviewed and applied according to the specific needs of the educators and education agencies using it and with full realization that it represents only one approach that might be taken, based on the research that was available at the time of publication. This practice guide should be used as a tool to assist in decision-making rather than as a " cookbook. " Any references within the document to specific education products are illustrative and do not imply endorsement of these products to the exclusion of other products that are not referenced. Alternative Formats On request, this publication can be made available in alternative formats, such as Braille, large print, audiotape, or computer diskette. For more information, call the Alternative Format Center at (202) 205-8113.
A Walk with SGD

We present novel empirical observations regarding how stochastic gradient descent (SGD) navigates the loss landscape of over-parametrized deep neural networks (DNNs). These observations expose the qualitatively different roles of learning rate and batch-size in DNN optimization and generalization. Specifically we study the DNN loss surface along the trajectory of SGD by interpolating the loss surface between parameters from consecutive iterations and tracking various metrics during training. We find that the loss interpolation between parameters before and after each training iteration’s update is roughly convex with a minimum (valley floor) in between for most of the training. Based on this and other metrics, we deduce that for most of the training update steps, SGD moves in valley like regions of the loss surface by jumping from one valley wall to another at a height above the valley floor. This ’bouncing between walls at a height’ mechanism helps SGD traverse larger distance for small batch sizes and large learning rates which we find play qualitatively different roles in the dynamics. While a large learning rate maintains a large height from the valley floor, a small batch size injects noise facilitating exploration. We find this mechanism is crucial for generalization because the valley floor has barriers and this exploration above the valley floor allows SGD to quickly travel far away from the initialization point (without being affected by barriers) and find flatter regions, corresponding to better generalization.
Injecting Relational Structural Representation in Neural Networks for Question Similarity

Effectively using full syntactic parsing information in Neural Networks (NNs) to solve relational tasks, e.g., question similarity, is still an open problem. In this paper, we propose to inject structural representations in NNs by (i) learning an SVM model using Tree Kernels (TKs) on relatively few pairs of questions (few thousands) as gold standard (GS) training data is typically scarce, (ii) predicting labels on a very large corpus of question pairs, and (iii) pre-training NNs on such large corpus. The results on Quora and SemEval question similarity datasets show that NNs trained with our approach can learn more accurate models, especially after fine tuning on GS.
The Impact of Industry 4 . 0 on Procurement and Supply Management : A Conceptual and Qualitative Analysis

The ongoing discussions about a „digital revolution― and ―disruptive competitive advantages‖ have led to the creation of such a business vision as ―Industry 4.0‖. Yet, the term and even more its actual impact on businesses is still unclear.This paper addresses this gap and explores more specifically, the consequences and potentials of Industry 4.0 for the procurement, supply and distribution management functions. A blend of literature-based deductions and results from a qualitative study are used to explore the phenomenon.The findings indicate that technologies of Industry 4.0 legitimate the next level of maturity in procurement (Procurement &Supply Management 4.0). Empirical findings support these conceptual considerations, revealing the ambitious expectations.The sample comprises seven industries and the employed method is qualitative (telephone and face-to-face interviews). The empirical findings are only a basis for further quantitative investigation , however, they support the necessity and existence of the maturity level. The findings also reveal skepticism due to high investment costs but also very high expectations. As recent studies about digitalization are rather rare in the context of single company functions, this research work contributes to the understanding of digitalization and supply management.