nils-herrmann/clean_scirepeval huggingface dataset:
New evidence from state panel data , 1977 to 2006

Economists have recently reexamined the “capital punishment deters homicide” thesis using modern econometric methods, with most studies reporting robust deterrent effects. The current study revisits this controversial question using annual state panel data from 1977 to 2006. Employing well-known econometric procedures for panel data analysis, our results provide no empirical support for the argument that the existence or application of the death penalty deters prospective offenders from committing homicide.
Applying SNMP Technology to Manage the Sensors in Internet of Things

Nowadays, the IoT is largely dependent on sensors. The IoT devices are embedded with sensors and have the ability to communicate. A variety of sensors play a key role in networked devices in IoT. In order to facilitate the management of such sensors, this paper investigates how to use SNMP protocol, which is widely used in network device management, to implement sensors information management of IoT system. The principles and implement details to setup the MIB file, agent and manager application are discussed. A prototype system is setup to validate our methods. The test results show that because of its easy use and strong expansibility, SNMP is suitable and a bright way for sensors information management of IoT system.
Odontogenic sinusitis: developments in diagnosis, microbiology, and treatment.

Odontogenic causes of sinusitis are frequently missed; clinicians often overlook odontogenic disease whenever examining individuals with symptomatic rhinosinusitis. Conventional treatments for chronic rhinosinusitis (CRS) will often fail in odontogenic sinusitis. There have been several recent developments in the understanding of mechanisms, diagnosis, and treatment of odontogenic sinusitis, and clinicians should be aware of these advances to best treat this patient population.



The majority of odontogenic disease is caused by periodontitis and iatrogenesis. Notably, dental pain or dental hypersensitivity is very commonly absent in odontogenic sinusitis, and symptoms are very similar to those seen in CRS overall. Unilaterality of nasal obstruction and foul nasal drainage are most suggestive of odontogenic sinusitis, but computed tomography is the gold standard for diagnosis. Conventional panoramic radiographs are very poorly suited to rule out odontogenic sinusitis, and cannot be relied on to identify disease. There does not appear to be an optimal sequence of treatment for odontogenic sinusitis; the dental source should be addressed and ESS is frequently also necessary to alleviate symptoms.



Odontogenic sinusitis has distinct pathophysiology, diagnostic considerations, microbiology, and treatment strategies whenever compared with chronic rhinosinusitis. Clinicians who can accurately identify odontogenic sources can increase efficacy of medical and surgical treatments and improve patient outcomes.
Meta Learning Shared Hierarchies

We develop a metalearning approach for learning hierarchically structured policies, improving sample efficiency on unseen tasks through the use of shared primitives—policies that are executed for large numbers of timesteps. Specifically, a set of primitives are shared within a distribution of tasks, and are switched between by task-specific policies. We provide a concrete metric for measuring the strength of such hierarchies, leading to an optimization problem for quickly reaching high reward on unseen tasks. We then present an algorithm to solve this problem end-to-end through the use of any off-the-shelf reinforcement learning method, by repeatedly sampling new tasks and resetting task-specific policies. We successfully discover1 meaningful motor primitives for the directional movement of four-legged robots, solely by interacting with distributions of mazes. We also demonstrate the transferability of primitives to solve long-timescale sparse-reward obstacle courses, and we enable 3D humanoid robots to robustly walk and crawl with the same policy.
A Survey on Game-Theoretic Approaches for Intrusion Detection and Response Optimization

Intrusion Detection Systems (IDS) are key components for securing critical infrastructures, capable of detecting malicious activities on networks or hosts. However, the efficiency of an IDS depends primarily on both its configuration and its precision. The large amount of network traffic that needs to be analyzed, in addition to the increase in attacks’ sophistication, renders the optimization of intrusion detection an important requirement for infrastructure security, and a very active research subject. In the state of the art, a number of approaches have been proposed to improve the efficiency of intrusion detection and response systems. In this article, we review the works relying on decision-making techniques focused on game theory and Markov decision processes to analyze the interactions between the attacker and the defender, and classify them according to the type of the optimization problem they address. While these works provide valuable insights for decision-making, we discuss the limitations of these solutions as a whole, in particular regarding the hypotheses in the models and the validation methods. We also propose future research directions to improve the integration of game-theoretic approaches into IDS optimization techniques.
A Cryptosystem Based on Hilbert Matrix using Cipher Block Chaining Mode

Cryptography is the science of using mathematics to encrypt and decrypt data. Cryptography enables you to store sensitive information or transmit it across insecure networks so that it cannot be read by anyone except the intended recipient. While cryptography is the science of securing data, cryptanalysis is the science of analyzing and breaking secure communication. Classical cryptanalysis involves an interesting combination of analytical reasoning, application of mathematical tools and pattern finding. The objectives of the proposed work are to propose a new cryptographic method based on the special matrix called the Hilbert matrix for authentication and confidentiality and to propose a model for confidentiality and authentication using a combination of symmetric and public cryptosystems. Further, it is extended to shared key cryptosystems with the concept of digital enveloping using a session key. In the present work an algorithm for shared key encryption is developed using Hilbert matrix cryptosystem. In this the block chaining modes of operation have been used to tackle the issues of confusion and diffusion.  Crypto System, Hilbert Matrix, Cipher Block Chain Encryption,Decryption.
Trading Strategies to Exploit Blog and News Sentiment

We use quantitative media (blogs, and news as a comparison) data generated by a large-scale natural language processing (NLP) text analysis system to perform a comprehensive and comparative study on how a company’s reported media frequency, sentiment polarity and subjectivity anticipates or reflects its stock trading volumes and financial returns. Our analysis provides concrete evidence that media data is highly informative, as previously suggested in the literature – but never studied on our scale of several large collections of blogs and news for over five years. Building on our findings, we give a sentiment-based market-neutral trading strategy which gives consistently favorable returns with low volatility over a five year period (2005-2009). Our results are significant in confirming the performance of general blog and news sentiment analysis methods over broad domains and sources. Moreover, several remarkable differences between news and blogs are also identified in this paper.
Bitcoin Transaction Malleability and MtGox

In Bitcoin, transaction malleability describes the fact that the signatures that prove the ownership of bitcoins being transferred in a transaction do not provide any integrity guarantee for the signatures themselves. This allows an attacker to mount a malleability attack in which it intercepts, modifies, and rebroadcasts a transaction, causing the transaction issuer to believe that the original transaction was not confirmed. In February 2014 MtGox, once the largest Bitcoin exchange, closed and filed for bankruptcy claiming that attackers used malleability attacks to drain its accounts. In this work we use traces of the Bitcoin network for over a year preceding the filing to show that, while the problem is real, there was no widespread use of malleability attacks before the closure of MtGox.
Evaluating Evaluation: Assessing Progress in Computational Creativity Research

Computational creativity research has produced many computational systems that are described as creative. A comprehensive literature survey reveals that although such systems are labelled as creative, there is a distinct lack of evaluation of the creativity of creative systems. As a research community, we should adopt a more scientific approach to evaluation of the creativity of our systems if we are to progress in understanding creativity and modelling it computationally. A methodology for creativity evaluation should accommodate different manifestations of creativity but also require a clear, definitive statement of the standards used for evaluation.  The approach is outlined and discussed, then illustrated through a comparative evaluation of the creativity of jazz improvisation systems.
Modulation of memory processes and cellular excitability in the dentate gyrus of freely moving rats by a 5-HT4 receptors partial agonist, and an antagonist.

Firstly, olfactory association learning was used to determine the modulating effect of 5-HT4 receptor involvement in learning and long-term memory. Secondly, the effects of systemic injections of a 5-HT4 partial agonist and an antagonist on long-term potentiation (LTP) and depotentiation in the dentate gyrus (DG) were tested in freely moving rats. The modulating role of the 5-HT4 receptors was studied by using a potent, 5-HT4 partial agonist RS 67333 [1-(4-amino-5-chloro-2-methoxyphenyl)-3-(1-n-butyl-4-piperidinyl)-1-propanone] and a selective 5-HT4 receptor antagonist RS 67532 [1-(4-amino-5-chloro-2-(3,5-dimethoxybenzyloxyphenyl)-5-(1-piperidinyl)-1-propanone]. Agonist or antagonist systemic chronic injections prior to five training sessions yielded a facilitatory effect on procedural memory during the first session only with the antagonist. Systemic injection of the antagonist only before the first training session improved procedural memory during the first session and associative memory during the second session. Similar injection with the 5-HT4 partial agonist had an opposite effect. The systemic injection of the 5-HT4 partial agonist prior to the induction of LTP in the dentate gyrus by high-frequency stimulation was followed by a population spike increase, while the systemic injection of the antagonist accelerated the depotentiation 48 h later. The behavioural and physiological results pointed out the involvement of 5-HT4 receptors in processing related to the long-term hippocampal-dependent memory system, and suggest that specific 5-HT4 agonists could be used to treat amnesic patients with a dysfunction in this particular system.
Analyzing the Effects of Adding Bitcoin to Portfolio

This paper analyses the effect of adding Bitcoin, to the portfolio (stocks, bonds, Baltic index, MXEF, gold, real estate and crude oil) of an international investor by using daily data available from 2nd of July, 2010 to 2nd of August, 2016. We conclude that adding Bitcoin to portfolio, over the course of the considered period, always yielded a higher Sharpe ratio. This means that Bitcoin’s returns offset its high volatility. This paper, recognizing the fact that Bitcoin is a relatively new asset class, gives the readers a basic idea about the working of the virtual currency, the increasing number developments in the financial industry revolving around it, its unique features and the detailed look into its continuously growing acceptance across different fronts (Banks, Merchants and Countries) globally. We also construct optimal portfolios to reflect the highly lucrative and largely unexplored opportunities associated with investment in Bitcoin. Portfolio management, Bitcoin, optimization, Sharpe ratio.
LIBRA: Lightweight Data Skew Mitigation in MapReduce

MapReduce is an effective tool for parallel data processing. One significant issue in practical MapReduce applications is data skew: the imbalance in the amount of data assigned to each task. This causes some tasks to take much longer to finish than others and can significantly impact performance. This paper presents LIBRA, a lightweight strategy to address the data skew problem among the reducers of MapReduce applications. Unlike previous work, LIBRA does not require any pre-run sampling of the input data or prevent the overlap between the map and the reduce stages. It uses an innovative sampling method which can achieve a highly accurate approximation to the distribution of the intermediate data by sampling only a small fraction of the intermediate data during the normal map processing. It allows the reduce tasks to start copying as soon as the chosen sample map tasks (only a small fraction of map tasks which are issued first) complete. It supports the split of large keys when application semantics permit and the total order of the output data. It considers the heterogeneity of the computing resources when balancing the load among the reduce tasks appropriately. LIBRA is applicable to a wide range of applications and is transparent to the users. We implement LIBRA in Hadoop and our experiments show that LIBRA has negligible overhead and can speed up the execution of some popular applications by up to a factor of 4.
Measurement of dietary intake in children.

When children and adolescents are the target population in dietary surveys many different respondent and observer considerations surface. The cognitive abilities required to self-report food intake include an adequately developed concept of time, a good memory and attention span, and a knowledge of the names of foods. From the age of 8 years there is a rapid increase in the ability of children to self-report food intake. However, while cognitive abilities should be fully developed by adolescence, issues of motivation and body image may hinder willingness to report. Ten validation studies of energy intake data have demonstrated that mis-reporting, usually in the direction of under-reporting, is likely. Patterns of under-reporting vary with age, and are influenced by weight status and the dietary survey method used. Furthermore, evidence for the existence of subject-specific responding in dietary assessment challenges the assumption that repeated measurements of dietary intake will eventually obtain valid data. Unfortunately, the ability to detect mis-reporters, by comparison with presumed energy requirements, is limited unless detailed activity information is available to allow the energy intake of each subject to be evaluated individually. In addition, high variability in nutrient intakes implies that, if intakes are valid, prolonged dietary recording will be required to rank children correctly for distribution analysis. Future research should focus on refining dietary survey methods to make them more sensitive to different ages and cognitive abilities. The development of improved techniques for identification of mis-reporters and investigation of the issue of differential reporting of foods should also be given priority.
Semantics and analysis of business process models in BPMN

The Business Process Modelling Notation (BPMN) is a standard for capturing business processes in the early phases of systems development. The mix of constructs found in BPMN makes it possible to create models with semantic errors. Such errors are especially serious, because errors in the early phases of systems development are among the most costly and hardest to correct. The ability to statically check the semantic correctness of models is thus a desirable feature for modelling tools based on BPMN. Accordingly, this paper proposes a mapping from BPMN to a formal language, namely Petri nets, for which efficient analysis techniques are available. The proposed mapping has been implemented as a tool that, in conjunction with existing Petri net-based tools, enables the static analysis of BPMN models. The formalisation also led to the identification of deficiencies in the BPMN standard specification.
Incremental effects of reward on experienced performance pressure : positive outcomes for intrinsic interest and creativity

Two field studies and a laboratory study examined the influence of reward for high performance on experienced performance pressure, intrinsic interest and creativity. Study 1 found that employees’ expected reward for high performance was positively related to performance pressure which, in turn, was positively associated with the employees’ interest in their jobs. Study 2 replicated this finding and showed that intrinsic interest, produced by performance pressure, was positively related to supervisors’ ratings of creative performance. Study 3 found that college students’ receipt of reward for high performance increased their experienced performance pressure which, in turn, was positively related to intrinsic interest and creativity.
Image denoising with norm weighted fusion estimators

In recent era, the weighted matrix rank minimization is used to reduce image noise, promisingly. However, low-rank weighted conditions may cause oversmoothing or oversharpening of the denoised image. This demands a clever engineering algorithm. Particularly, to remove heavy noise in image is always a challenging task, specially, when there is need to preserve the fine edge structures. To attain a reliable estimate of heavy noise image, a norm weighted fusion estimators method is proposed in wavelet domain. This holds the significant geometric structure of the given noisy image during the denoising process. Proposed method is applied on standard benchmark images, and simulation results outperform the most popular rivals of noise reduction approaches, such as BM3D, EPLL, LSSC, NCSR, SAIST, and WNNM in terms of the quality measurement metric PSNR (dB) and structural analysis SSIM indices.
Qualitatively characterizing neural network optimization problems

Training neural networks involves solving large-scale non-convex optimization problems. This task has long been believed to be extremely difficult, with fear of local minima and other obstacles motivating a variety of schemes to improve optimization, such as unsupervised pretraining. However, modern neural networks are able to achieve negligible training error on complex tasks, using only direct training with stochastic gradient descent. We introduce a simple analysis technique to look for evidence that such networks are overcoming local optima. We find that, in fact, on a straight path from initialization to solution, a variety of state of the art neural networks never encounter any significant obstacles.
Small Sample Learning in Big Data Era

As a promising area in artificial intelligence, a new learning paradigm, called Small Sample Learning (SSL), has been attracting prominent research attention in the recent years. In this paper, we aim to present a survey to comprehensively introduce the current techniques proposed on this topic. Specifically, current SSL techniques can be mainly divided into two categories. The first category of SSL approaches can be called “concept learning”, which emphasizes learning new concepts from only few related observations. The purpose is mainly to simulate human learning behaviors like recognition, generation, imagination, synthesis and analysis. The second category is called “experience learning”, which usually co-exists with the large sample learning manner of conventional machine learning. This category mainly focuses on learning with insufficient samples, and can also be called small data learning in some literatures. More extensive surveys on both categories of SSL techniques are introduced and some neuroscience evidences are provided to clarify the rationality of the entire SSL regime, and the relationship with human learning process. Some discussions on the main challenges and possible future research directions along this line are also presented.
Constructing Risk Measures from Uncertainty Sets

We illustrate the correspondence between uncertainty sets in robust optimization and some popular risk measures in finance, and show how robust optimization can be used to generalize the concepts of these risk measures. We also show that by using properly defined uncertainty sets in robust optimization models, one can construct coherent risk measures. Our results have implications for efficient portfolio optimization under different measures of risk. Department of Mathematics, National University of Singapore, Singapore 117543.  The research of the author was partially supported by Singapore-MIT Alliance, NUS Risk Management Institute and NUS startup grants R-146-050-070-133 & R146-050-070-101. Division of Mathematics and Sciences, Babson College, Babson Park, MA 02457, USA. E-mail: dpachamanova@babson.edu. Research supported by the Gill grant from the Babson College Board of Research. NUS Business School, National University of Singapore.  dscsimm@nus.edu.sg. The research of the author was partially supported by Singapore-MIT Alliance, NUS Risk Management Institute and NUS academic research grant R-314-000-066-122 and R-314-000-068-122.
Smart Contracts

Smart Contracts sind rechtliche Vereinbarungen, die sich IT-Technologien bedienen, um die eigene Durchsetzbarkeit sicherzustellen. Es werden durch Smart Contracts autonom Handlungen initiiert, die zuvor vertraglich vereinbart wurden. Beispielsweise können vereinbarte Zahlungen von Geldbeträgen selbsttätig veranlasst werden. Basieren Smart Contracts auf Blockchains, ergeben sich per se vertrauenswürdige Transaktionen. Eine dritte Instanz zur Sicherstellung einer korrekten Transaktion, beispielsweise eine Bank oder ein virtueller Marktplatz, wird nicht benötigt. Echte Peer-to-Peer-Verträge sind möglich. Ein weiterer Anwendungsfall von Smart Contracts ist denkbar. Smart Contracts könnten statt Vereinbarungen von Vertragsparteien gesetzliche Regelungen ausführen. Beispielsweise die Regelungen des Patentgesetzes könnten durch einen Smart Contract implementiert werden. Die Verwaltung von IPRs (Intellectual Property Rights) entsprechend den gesetzlichen Regelungen würde dadurch sichergestellt werden. Bislang werden Spezialisten, beispielsweise Patentanwälte, benötigt, um eine akkurate Administration von Schutzrechten zu gewährleisten. Smart Contracts könnten die Dienstleistungen dieser Spezialisten auf dem Gebiet des geistigen Eigentums obsolet werden lassen.
Hybrid Recommendation System for Tourism

This paper adopts item-based collaborative filtering to predict the interests of an active tourist by collecting preferences or taste information from a number of other tourists. Our proposed mechanism is able to predict a set recommended tourism places of elicited rating places (e.g., ratings of 1 through 5 stars) for the active tourist pre-traveling places. Furthermore, giving restriction of traveling factors, such as budge and time, the recommendation system will refine the exact set of tourism places by applying genetic algorithm mechanism. Finally, the system is based on minimum cost to schedule traveling path from a set of selected places by the using genetic algorithm approach. Our proposed hybrid recommendation algorithm focuses on the refining efficiency and provides multi-functional tourism information.