nils-herrmann/clean_scirepeval huggingface dataset:
Accuracy in Detecting Failure in Ballscrew Assessment Towards Machine Tool Servitization
Year: 2019

Many manufacturers, in particular machine tool builders, aim for service innovation in order to improve their market competitiveness. Machine tool trading companies expect to create a value added process through shifting from just selling product i.e. machine tools to selling an integrated combination of product and services, so called product service system (PSS) which in turn competes with other developing companies. This is the process of servitization. The purpose of this work is to explore the benefits of servitizing machine tool industry and identify the effect of such an approach on the performance of engineering companies. As important, is to evaluate the accuracy of components as part of predictive maintenance, to support machine tool sevitization. This paper proposed a case study of ballscrew performance assessment. The purpose was monitoring the degradation of two parallel ball screws in a 5 Axis gantry machine tool, based on data from an acoustic emission sensor and machine learning technology. The significance of this work is that ballscrew performance as part of machine tool accuracy is critical for high value manufacturing. Machine tool users would prefer their machines to be maintained to the required tolerance by the predictive maintenance service warranty offered by the machine trader and keep their machines up to date to reduce unnecessary downtime.
Deep Inductive Network Representation Learning
Venue: WWW
Year: 2018

This paper presents a general inductive graph representation learning framework called DeepGL for learning deep node and edge features that generalize across networks. In particular, DeepGL begins by deriving a set of base features from the graph (e.g. graphlet features) and automatically learns a multi layered hierarchical graph representation where each successive layer leverages the output from the previous layer to learn features of a higher order. Contrary to previous work, DeepGL learns relational functions (each representing a feature) that naturally generalize across networks and are therefore useful for graph based transfer learning tasks. Moreover, DeepGL naturally supports attributed graphs, learns interpretable inductive graph representations, and is space efficient (by learning sparse feature vectors) In addition, DeepGL is expressive, flexible with many interchangeable components, efficient with a time complexity of O(|E| and scalable for large networks via an efficient parallel implementation. Compared with recent methods, DeepGL is (1) effective for across network transfer learning tasks and large (attributed) graphs, (2) space efficient requiring up to 6x less memory, (3) fast with up to 182x speedup in runtime performance, and (4) accurate with an average improvement in AUC of 20% or more on many learning tasks and across a wide variety of networks.
Pour une revolution fiscale un impot sur le revenu pour le XXIe siecle
Year: 2011

La fiscalite francaise est asphyxiee par sa complexite, son manque de transparence et l'accumulation de privileges pour une minorite de contribuables ultra riches. Mais on en reste trop souvent, en la matiere, a des enonces aussi vagues que steriles. Ce livre innove en proposant une critique d'ensemble du systeme fiscal francais. Il demontre scientifiquement, pour la premiere fois, le caractere regressif de l'impot dans notre pays (ce qui signifie que, tous prelevements confondus, les taux d'imposition sont plus eleves pour les menages les plus modestes et s'abaissent pour les plus riches) Pour cette raison, il fera date. Mais cette analyse au scalpel ne se contente pas de mettre au jour l'injustice du systeme. Elle plaide pour une revolution fiscale, chiffree et operationnelle, fondee sur trois principes: equite, progressivite reelle, democratie. Ce livre contribue de maniere decisive a l'edification d'une nouvelle critique sociale et se pose au centre du debat politique pour les annees a venir. Pour la premiere fois dans le monde, un site Internet permet a chacun d'evaluer les propositions des auteurs et de concevoir une reforme www.revolution fiscale.fr (presentation editeur)
Hacia una poetica aporetica: de la interpretacion a la traduccion en cuatro pasajes de En Defensa de Palamedes de Gorgias
Year: 2018

espanolEn Defensa de Palamedes de Gorgias es un texto que reune, simultaneamente, voces complementarias y antagonicas: voces de la retorica escolar, del drama, de la filosofia, de la sofistica y de la politica de finales del siglo V a.C. en Atenas. Otro rasgo particular es el tratamient o del discurso del heroe cuya retorica puede representar la tragedia comunicativa del lenguaje o su superacion. En este trabajo presentaremos algunas problematicas del texto gorgiano a la luz de dos instancias que se implican pero no siempre se condicen: la traduccion y la interpretacion critica. A partir del criterio de traduccion de la "letra " de B ERMAN (1991) se analizaran pasajes de diferentes versiones italianas del texto: Unter steiner (1949 Mazzara (1999) Mariani (2010) y Giombini (2012 EnglishDefense of Palamedes by Gorgias is a text that brings together complementary and antagonistic voices: voices of the school rhetoric of drama, philosophy, sophist ideas and politics of the late 5th century BC. in Athens. Another particular feature is the treatment of the hero's speech, whose rhetoric may represent the communicative tragedy of language or its impr ovement. This paper presents some peculiarities of the Gorgian text in the light of two fields that study it but not always coincide: translation and critical interpretation. Based on Berman's translation criteria (1991) four passages of different Italian translations will be an alyzed: Untersteiner (1949 Mazzara (1999) Mariani (2010) and Giombini (20
Theoretical performance estimation of shrouded twin rotor wind turbines using the actuator disk theory
Venue: Renewable Energy
Year: 2019

Wind energy is anticipated to play a vital role to fulfill the worldwide energy requirements, whereas existing bare wind turbines can convert only a fraction of flow energy into electricity. To bridge the gap between the escalating demand and the generation capability, we propose a shrouded twin rotor turbine design, whose power coefficient exceeds the Betz Joukowsky limit. We analyze the flow through the wind turbine assembly, using the actuator disk theory, and estimate the power output for a pair of rotor loading coefficients. We find the existence of two regimes in which the proposed design performs better than the single rotor configuration: (a) a turbine turbine mode where both rotors work as turbines and (b) a turbine fan mode where one rotor is a turbine while the other is a fan an idea proposed by Betz (A. Betz, Wind Energie und ihre Ausnutzung durch Windmuhlen, Vandenhoeck, 1926) Both modes enable achieving maximum power for multiple combinations of the loading coefficient pair. Moreover, power output depends solely on a single parameter, defined using the area weighted sum of the loading coefficients. We derive the optimum performance criterion and present the effects of the shroud geometry, back pressure, and flow efficiency parameters on the power output and the performance envelope.
Recovery of nitric and acetic acids from etching waste solutions using a synergistic system consisting of N235 and TRPO in cyclohexane
Venue: Hydrometallurgy
Year: 2019

To purify and recover phosphoric acid (H3PO4) from laboratory synthetic etching waste, Alamine 336 (N235) and trialkyl phosphine oxide (TRPO) were used in a mixed extractant to extract nitric (HNO3) and acetic acids (HAc) The effects of the extraction temperature, extractant concentration, and ratio of the organic phase to the liquid phase on the separation process were investigated. Using an organic system containing 3.5% (v/v) N235, 46.5% (v/v) TRPO, and 50% (v/v) cyclohexane, 70% of the HAc and 85% of the HNO3 were extracted, with 8.5% H3PO4 co extracted through single stage contact. Furthermore, more than 99% HAc and 99.5% HNO3 were removed after three counter current extractions, with only ~15% of the H3PO4 being co extracted. A stripping rate of more than 99% was achieved for HNO3, HAc, and H3PO4 using 0.25 mol*L 1 NH3*H2O. UV vis and FT IR analyses indicated that the N235 and TRPO mixture had a synergistic effect on HNO3 and HAc extraction. The mechanisms of HNO3 and HAc extraction involve ion pair association and hydrogen bonding, respectively.
The Status, Challenges and Opportunities of Kalamay Festival in San Enrique, Iloilo
Year: 2020

Events and festivals are a global phenomenon in a competitive industry that has experienced rapid growth. Festivals attract tourists and create festival tourism. San Enrique, Iloilo celebrates its Kalamay Festival every month of July. Kalamay Festival, as the primary subject of this research study, is a celebration of the town's sugarcane farming and the production of raw sugar as well as the impact of the sugar industry on the town for almost 40 years. With its current situation, it does not have a high level of market oriented operation, public awareness, and public participation. The management of Kalamay Festival requires a well written plan to greater guarantee sustainability in continuing such an event. The study aimed to develop a festival development plan to provide a direction in assessing the status of Kalamay Festival in terms of economic, social, environmental, and cultural aspects, including the opportunities and challenges of Kalamay Festival in terms of accessibility, accommodation, attraction, amenities, and awareness. The findings of the study served as a basis for the formulation of a three year festival development plan that will help guide in identifying areas for development, and formulating programs to aid the needs of the Local Tourism Office of San Enrique, Iloilo that will facilitate the continuous improvement of the Kalamay Festival, to be well recognized in the local and international tourism market.
GrapAL: Querying Semantic Scholar's Literature Graph
Venue: ArXiv
Year: 2019

We introduce GrapAL (Graph database of Academic Literature) a versatile tool for exploring and investigating scientific literature which satisfies a variety of use cases and information needs requested by researchers. At the core of GrapAL is a Neo4j graph database with an intuitive schema and a simple query language. In this paper, we describe the basic elements of GrapAL, how to use it, and several use cases such as finding experts on a given topic for peer reviewing, discovering indirect connections between biomedical entities, and computing citation based metrics.
Towards an Explanation Space to Align Humans and Explainable AI Teamwork
Venue: ArXiv
Year: 2021

Providing meaningful and actionable explanations to end users is a fundamental prerequisite for implementing explainable intelligent systems in the real world. Explainability is a situated interaction between a user and the AI system rather than being static design principles. The content of explanations is context dependent and must be defined by evidence about the user and its context. This paper seeks to operationalize this concept by proposing a formative architecture that defines the explanation space from a user inspired perspective. The architecture comprises five intertwined components to outline explanation requirements for a task: (1) the end users mental models, (2) the end users cognitive process, (3) the user interface, (4) the human explainer agent, and the (5) agent process. We first define each component of the architecture. Then we present the Abstracted Explanation Space, a modeling tool that aggregates the architecture's components to support designers in systematically aligning explanations with the end users work practices, needs, and goals. It guides the specifications of what needs to be explained (content end users mental model) why this explanation is necessary (context end users cognitive process) to delimit how to explain it (format human explainer agent and user interface) and when should the explanations be given. We then exemplify the tool's use in an ongoing case study in the aircraft maintenance domain. Finally, we discuss possible contributions of the tool, known limitations/areas for improvement, and future work to be done.   Interdisciplinary study; Human AI Teaming 1 ar X iv :2 10 6. 01 50 3v 1 cs .A I] 2 J un 2 02 1
Employees' Job Satisfaction and Performance through Working from Home during the Pandemic Lockdown
Year: 2020

The pandemic lockdown provided the impetus for working from home or telework. The survey objective was to gauge the employees' job satisfaction and performance while working from home. Responses from 503 (80.9% administrative staff from Universiti Teknologi MARA Cawangan Selangor, Malaysia, were analyzed. The findings indicated that 87.1% were satisfied with working from home; however, the work performance, measured through the availability of ICT peripherals, showed a dismaying 53.7% The implications from the findings require university administrators to reassess the policy for working from home, that is, only employees with adequate ICT equipment can be allowed to telework.  work from home, telework, job satisfaction, work performance eISSN: 2398 4287(c) 2020.   and cE Bs (Centre for Environment Behaviour Studies) Faculty of Architecture, Planning Surveying, Universiti Teknologi MARA, Malaysia.
Multidimensional Perfectionism and the Big Five Personality Traits: A Meta Analysis
Venue: European Journal of Personality
Year: 2019

Multidimensional perfectionism includes the dimensions perfectionistic concerns and perfectionistic strivings. Many studies have investigated the nomological network of multidimensional perfectionism by relating perfectionistic concerns and perfectionistic strivings to the Big Five personality traits. Results from these studies were largely inconsistent. In the present study, we meta analytically integrated 672 effect sizes from 72 samples (N 21 573) describing relations between multidimensional perfectionism and the Big Five personality traits. Perfectionistic concerns correlated positively with Neuroticism (r 0.383) and negatively with Extraversion (r 0.198) Agreeableness (r 0.198) Conscientiousness (r 0.111) and Openness (r 0.087) Perfectionistic strivings correlated positively with Conscientiousness (r 0.368) Openness (r 0.121) Neuroticism (r 0.090) and Extraversion (r 0.067) and were unrelated to Agreeableness (r 0.002) The measures of perfectionistic concerns and perfectionistic strivings moderated most of these relations. Meta analytic structural equation modelling allowed controlling each perfectionism dimension for the respective other. This partialling increased all correlations with the exception of the previously positive correlation between perfectionistic strivings and Neuroticism, which ceased to be significant. The findings support the distinction between perfectionistic strivings and perfectionistic concerns and demonstrate how multidimensional perfectionism is situated in the context of broader personality traits.
Structural change as a key component for agricultural non CO2 mitigation efforts
Venue: Nature Communications
Year: 2018

Agriculture is the single largest source of anthropogenic non carbon dioxide (non CO2) emissions. Reaching the climate target of the Paris Agreement will require significant emission reductions across sectors by 2030 and continued efforts thereafter. Here we show that the economic potential of non CO2 emissions reductions from agriculture is up to four times as high as previously estimated. In fact, we find that agriculture could achieve already at a carbon price of 25 /tCO2eq non CO2 reductions of around 1 GtCO2eq/year by 2030 mainly through the adoption of technical and structural mitigation options. At 100 /tCO2eq agriculture could even provide non CO2 reductions of 2.6 GtCO2eq/year in 2050 including demand side efforts. Immediate action to favor the widespread adoption of technical options in developed countries together with productivity increases through structural changes in developing countries is needed to move agriculture on track with a 2 degC climate stabilization pathway.To achieve the climate target of the Paris Agreement substantial emission reductions will be required across economic sectors. Here the authors show that agriculture can make a significant contribution to non CO2 mitigation efforts through structural change in the livestock sector and the deployment of technical options.
Inflammation and ovarian cancer.
Venue: Cytokine
Year: 2012

Epithelial ovarian cancer (EOC) is a highly lethal gynecological cancer for which overall prognosis has remained poor over the past few decades. A number of theories have been postulated in an effort to explain the etiology of EOC. Noteworthy, these theories likely are not mutually exclusive, as they all converge more or less on the role of inflammation in promoting ovarian tumorigenesis and cancer progression. The tumor milieu in which ovarian carcinoma develops has been described as one enriched with a broad spectrum of pro inflammatory cytokines and chemokines. In particular, several of these cytokines, such as tumor necrosis factor (TNF) a, interleukin (IL) 1b and IL 6, produced by tumor itself or/and activated immune cells, besides stimulating cancer cell growth, have been shown to influence clinical disease status and prognosis, by reducing responsiveness to chemotherapy and inducing symptoms such as anorexia, altered energy metabolism, anemia, weight loss, depression and fatigue. Recent data show that cytokine antagonists may have a role to play in the treatment of ovarian cancer. Their action by inhibiting both production and activity of inflammatory cytokines seems to obtain the control of angiogenetic and apoptotic events, the reversal of chemoresistance, the improvement of systemic symptoms and prognosis. In the light of our scientific research and the most recent experimental and clinical advances, our review will discuss the most relevant and recent findings on the role of proinflammatory cytokines in the pathogenesis and prognosis of ovarian cancer and the possible therapeutic implications.
Eduardo VIVEIROS DE CASTRO, Politique des multiplicites. Pierre Clastres face a l'Etat
Year: 2020

L'anthropologue bresilien Eduardo Viveiros de Castro compte parmi les principales figures du renouveau de l'ethnologie americaniste. Il a initie ce qu'il est convenu d'appeler le tournant ontologique en anthropologie avec ses concepts de multinaturalisme et de perspectivisme qui soumettent a la critique le dualisme entre nature et culture, incapable de decrire les cosmologies amazoniennes. Encore peu traduits, les travaux de Viveiros de Castro attestent pourtant du lien qui s'est noue, depuis.
(Z) 1 (4 Methylphenyl) 2 (phenylsulfonyl)ethanone oxime
Venue: Acta crystallographica. Section E, Structure reports online
Year: 2012

The molecule of the title compound, C15H15NO3S, has a twisted U shaped conformation: the twist occurs at the central C S(=O)2 C C C unit and the benzene ring makes a dihedral angle of 28.74 (7)deg with the phenyl ring. The S C C=N torsion angle is 88.95 (13)deg. In the crystal, inversion dimers linked by pairs of O HN hydrogen bonds generate R 2 2(6) loops, and C HO hydrogen bonds connect the dimers into a three dimensional network.
A composite learning method for multi ship collision avoidance based on reinforcement learning and inverse control
Venue: Neurocomputing
Year: 2020

Model free reinforcement learning methods have potentials in ship collision avoidance under unknown environments. To defect the low efficiency problem of the model free reinforcement learning, a composite learning method is proposed based on an asynchronous advantage actor critic (A3C) algorithm, a long short term memory neural network (LSTM) and Q learning. The proposed method uses Q learning for adaptive decisions between a LSTM inverse model based controller and the model free A3C policy. Multi ship collision avoidance simulations are conducted to verify the effectiveness of the model free A3C method, the proposed inverse model based method and the composite learning method. The simulation results indicate that the proposed composite learning based ship collision avoidance method outperforms the A3C learning method and a traditional optimization based method.
Browser Extension based Hybrid Anti Phishing Framework using Feature Selection
Year: 2019

Phishing is one of the socially engineered cyberse curity attacks where the attacker impersonates a genuine and legitimate website source and sends emails with the intention of stealing sensitive personal information. The phishing websites' URLs are usually spread through emails by luring the users to click on them or by embedding the link to fake website replicating any genuine e commerce website inside the invoice or other documents. The phishing problem is very wide and no single solution exists to mitigate all the vulnerabilities properly. Thus, multiple techniques are often combined and implemented to mitigate specific attacks. The primary objective of this paper is to propose an efficient and effective anti phishing solution that can be implemented at the client side in the form of a browser extension and should be capable to handle real time scenarios and zero day attacks. The proposed approach works efficiently for any phishing link carrier mode as the execution on clicking on any link or manually entering URL in the browser doesn't proceed unless the proposed framework approves that the website associated with that URL is genuine. Also, the proposed framework is capable to handle DNS cache poisoning attacks even if the system's DNS cache is somehow infected. This paper first presents a comprehensive review that broadly discusses the phishing life cycle and available anti phishing countermeasures. The proposed framework considers the pros and cons of existing methodologies and presents a robust solution by combining the best features to ensure that a fast and accurate response is achieved. The effectiveness of the approach is tested in a real time dataset consisting of live phishing and legitimate website URLs and the framework is found to be 98.1% accurate in identifying websites correctly in very less time.