nils-herrmann/clean_scirepeval huggingface dataset:
A systematic mapping on gamification applied to education
Venue: SAC
Year: 2014

Gamification is a term that refers to the use of game elements in non game contexts with the goal of engaging people in a variety of tasks. There is a growing interest in gamification as well as its applications and implications in the field of Education since it provides an alternative to engage and motivate students during the process of learning. Despite this increasing interest, to the best of our knowledge, there are no studies that cover and classify the types of research being published and the most investigated topics in the area. As a first step towards bridging this gap, we carried out a systematic mapping to synthesize an overview of the area. We went through 357 papers on gamification. Among them, 48 were related to education and only 26 met the criteria for inclusion and exclusion of articles defined in this study. These 26 papers were selected and categorized according to their contribution. As a result, we provide an overview of the area. Such an overview suggests that most studies focus on investigating how gamification can be used to motivate students, improve their skills, and maximize learning.
Simulator and scenario for "Is the Market Really a Good Teacher? Market Selection, Collective Adaptation and Financial Instability"
Year: 2016

Jamel (Java Agent based Macro Economic Laboratory) is an open source agent based framework dedicated to the modeling, the simulation and the analysis of complex monetary economies.  (20160509) contains the scenario used in the paper "Is the market really a good teacher? Market selection, collective adaptation and financial instability" by Pascal Seppecher, Isabelle Salle and Dany Lang.
ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning
Venue: ArXiv
Year: 2020

When building machine learning models using sensitive data, organizations should ensure that the data processed in such systems is adequately protected. For projects involving machine learning on personal data, Article 35 of the GDPR mandates it to perform a Data Protection Impact Assessment (DPIA) In addition to the threats of illegitimate access to data through security breaches, machine learning models pose an additional privacy risk to the data by indirectly revealing about it through the model predictions and parameters. Guidances released by the Information Commissioner's Office (UK) and the National Institute of Standards and Technology (US) emphasize on the threat to data from models and recommend organizations to account for and estimate these risks to comply with data protection regulations. Hence, there is an immediate need for a tool that can quantify the privacy risk to data from models. In this paper, we focus on this indirect leakage about training data from machine learning models. We present ML Privacy Meter, a tool that can quantify the privacy risk to data from models through state of the art membership inference attack techniques. We discuss how this tool can help practitioners in compliance with data protection regulations, when deploying machine learning models.
A trajectory privacy preserving scheme based on a dual K mechanism for continuous location based services
Venue: Inf. Sci.
Year: 2020

Location based services (LBSs) have increasingly provided by a broad range of devices and applications, but one associated risk is location disclosure. To solve this problem, a commonly method is to adopt K anonymity in the centralized architecture based on a single trusted anonymizer. However, this strategy may compromise user privacy involving continuous LBSs. In this study, we propose a dual K mechanism (DKM) to protect the users' trajectory privacy for continuous LBSs. The proposed DKM method firstly inserted multiple anonymizers between the user and the location service provider (LSP) and K query locations are sent to different anonymizers to achieve K anonymity. Simultaneously, we combined the dynamic pseudonym and the location selection mechanisms to improve user trajectory privacy. Hence, neither the LSP nor the anonymizer can obtain the user trajectory. Security analyses demonstrates that our proposed scheme can effectively enhance user trajectory privacy protection, and the simulation results prove that the DKM scheme can preserve user trajectory privacy with low overhead on a single anonymizer.
Can Reputation Discipline the Gig Economy? Experimental Evidence From an Online Labor Market
Year: 2019

Just as employers face uncertainty when hiring workers, workers also face uncertainty when accepting employment, and bad employers may opportunistically depart from expectations, norms, and laws. However, prior research in economics and information sciences has focused sharply on the employer's problem of identifying good workers rather than vice versa. This issue is especially pronounced in markets for gig work, including online labor markets, where platforms are developing strategies to help workers identify good employers. We build a theoretical model for the value of such reputation systems and test its predictions in on Amazon Mechanical Turk, where employers may decline to pay workers while keeping their work product and workers protect themselves using third party reputation systems, such as Turkopticon. We find that: (1) in an experiment on worker arrival, a good reputation allows employers to operate more quickly and on a larger scale without loss to quality; (2) in an experimental audit of employers, working for good reputation employers pays 40 percent higher effective wages due to faster completion times and lower likelihoods of rejection; and (3) exploiting reputation system crashes, the reputation system is particularly important to small, good reputation employers, which rely on the reputation system to compete for workers against more established employers. This is the first clean field evidence of the effects of employer reputation in any labor market and is suggestive of the special role that reputation diffusing technologies can play in promoting gig work, where conventional labor and contract laws are weak.
Data Driven Threat Hunting Using Sysmon
Venue: ICCSP
Year: 2018

Threat actors can be persistent, motivated and agile, and they leverage a diversified and extensive set of tactics, techniques, and procedures to attain their goals. In response to that, organizations establish threat intelligence programs to improve their defense capabilities and mitigate risk. Actionable threat intelligence is integrated into security information and event management systems (SIEM) forming a threat intelligence platform. A threat intelligence platform aggregates log data from multiple disparate sources by deploying numerous collection agents and provides centralized analysis and reporting of an organization's security events for identifying malicious activity. Sysmon logs is a data source that has received considerable attention for endpoint visibility. Approaches for threat detection using Sysmon have been proposed mainly focusing on search engines (NoSQL database systems) This paper presents a new automated threat assessment system that relies on the analysis of continuous incoming feeds of Sysmon logs. The system is based on a cyber threat intelligence ontology and analyses Sysmon logs to classify software in different threat levels and augment cyber defensive capabilities through situational awareness, prediction, and automated courses of action.
A study on factors influencing the rural consumer while purchasing FMCG products
Year: 2021

First moving consumer goods (FMCG) also known as consumer packaged goods are consumed every day by the average consumer and used up over short period of days, weeks or months and within one year. The items in FMCG include all consumables (other than groceries, pulses) that people buy at regular intervals. Rural consumers' incomes are rising and willing to buy products which improve their lifestyle. With this backdrop the study entitled "A study on factors influencing the rural consumer while purchasing FMCG products" was undertaken to analyze the factors affecting the buying behaviour of rural consumers towards FMCG products and their decision making process, level according to the attributes like price, quality, free offers, packaging, replace facility, quantity, availability, variety, purchase experience with regard to FMCGs. The product chosen for this study are dental care, face powder, detergent powder and soft drink. This study reveals that how a rural consumer make decision to spend their available resources (time, effort, money) on consumption related item, their reactions towards different product features, price and advertisement, in order to ensure strong competitive advantage and their satisfaction level towards different attribute of FMCG products like price, quality, availability quantity etc. The present study concluded that the consumers prefer to buy FMCG brands from haat/mandi. Majority of the consumers stick to particular brand for more time. Advertisement is the most influencing factor while buying any FMCG brand.
Rituximab therapy for acute humoral rejection after kidney transplantation.
Venue: Transplantation
Year: 2007

A pilot study was performed on eight consecutive renal transplant (RT) patients presenting with acute humoral rejection (AHR) to assess the efficacy of monoclonal anti B cell antibodies, such as rituximab (375 mg/m weekly) for 3 to 5 consecutive weeks, in addition to plasma exchange (PE) steroids, mycophenolate mofetil, and tacrolimus. AHR was associated with increased serum creatinine, the appearance of donor specific alloantibodies (DSA) and the presence of C4d in a transplant biopsy. After a follow up of 10 months (range 7 23) patient and graft survivals were 100% and 75% respectively. Renal function improved in six cases in which serum creatinine decreased from 297+ 140 to 156+ 53 micromol/L (P=0.015) graft loss occurred in two cases; and four patients had infectious complications. At last follow up, DSA had disappeared or decreased in four cases. Rituximab therapy, in addition to PE, might be of benefit for RT patients presenting with AHR.
Language ID in the Wild: Unexpected Challenges on the Path to a Thousand Language Web Text Corpus
Venue: COLING
Year: 2020

Large text corpora are increasingly important for a wide variety of Natural Language Processing (NLP) tasks, and automatic language identification (LangID) is a core technology needed to collect such datasets in a multilingual context. LangID is largely treated as solved in the literature, with models reported that achieve over 90% average F1 on as many as 1,366 languages. We train LangID models on up to 1,629 languages with comparable quality on held out test sets, but find that human judged LangID accuracy for web crawl text corpora created using these models is only around 5% for many lower resource languages, suggesting a need for more robust evaluation. Further analysis revealed a variety of error modes, arising from domain mismatch, class imbalance, language similarity, and insufficiently expressive models. We propose two classes of techniques to mitigate these errors: wordlist based tunable precision filters (for which we release curated lists in about 500 languages) and transformer based semi supervised LangID models, which increase median dataset precision from 5.5% to 71.2% These techniques enable us to create an initial data set covering 100K or more relatively clean sentences in each of 500+ languages, paving the way towards a 1,000 language web text corpus.
Quantum chaos dynamics in long range power law interaction systems
Venue: Physical Review B
Year: 2019

We use out of time order commutator (OTOC) to diagnose the propagation of chaos in one dimensional long range power law interaction system. We map the evolution of OTOC to a classical stochastic dynamics problem and use a Brownian quantum circuit to exactly derive the master equation. We vary two parameters: the number of qubits $N$ on each site (the onsite Hilbert space dimension) and the power law exponent \alpha$ Three light cone structures of OTOC appear at $N 1$ (1) logarithmic when $0.5<\alpha\lesssim 0.8$ (2) sublinear power law when $0.8 \lesssim \alpha \lesssim 1.5$ and (3) linear when \alpha \gtrsim 1.5$ The OTOC scales as \exp(\lambda t)/x^{2\alpha} and $t^{2 \alpha \zeta} x^ 2 \alpha} respectively beyond the light cones in the first two cases. When \alpha \geq 2$ the OTOC has essentially the same diffusive broadening as systems with short range interactions, suggesting a complete recovery of locality. In the large $N$ limit, it is always a logarithmic light cone asymptotically, although a linear light cone can appear before the transition time for \alpha \gtrsim 1.5$ This implies the locality is never fully recovered for finite \alpha$ Our result provides a unified physical picture for the chaos dynamics in long range power law interaction system.
Maternal N Acetylcysteine Therapy Prevents Hypertension in Spontaneously Hypertensive Rat Offspring: Implications of Hydrogen Sulfide Generating Pathway and Gut Microbiota
Venue: Antioxidants
Year: 2020

Hypertension can come from early life. N acetylcysteine (NAC) a hydrogen sulfide (H2S) precursor as well as an antioxidant, has antihypertensive effect. We investigated whether maternal NAC therapy can protect spontaneously hypertensive rats (SHR) male offspring against hypertension. The pregnant rats were assigned to four groups: SHRs without treatment; Wistar Kyoto (WKY) without treatment; SHR+NAC, SHRs received 1% NAC in drinking water throughout pregnancy and lactation; and, WKY+NAC, WKY rats received 1% NAC in drinking water during pregnancy and lactation. Male offspring (n 8/group) were killed at 12 weeks of age. Maternal NAC therapy prevented the rise in systolic blood pressure (BP) in male SHR offspring at 12 weeks of age. Renal cystathionine b synthase (CBS) and 3 mercaptopyruvate sulphurtransferase (3MST) protein levels and H2S releasing activity were increased in the SHR+NAC offspring. Maternal NAC therapy increased fecal H2S and thiosulfate levels in the SHR+NAC group. Additionally, maternal NAC therapy differentially shaped gut microbiota and caused a distinct enterotype in each group. The protective effect of maternal NAC therapy against hypertension in SHR offspring is related to increased phylum Actinobacteria and genera Bifidobacterium and Allobaculum, but decreased phylum Verrucomicrobia, genera Turicibacter, and Akkermansia. Several microbes were identified as microbial markers, including genera Bifidobacterium, Allobaculum, Holdemania, and Turicibacter. Our results indicated that antioxidant therapy by NAC in pregnant SHRs can prevent the developmental programming of hypertension in male adult offspring. Our findings highlight the interrelationships among H2S generating pathway in the kidneys and gut, gut microbiota, and hypertension. The implications of maternal NAC therapy elicited long term protective effects on hypertension in later life that still await further clinical translation.
Manufacturing method of high power super energy storage power supply
Year: 2012

The invention discloses a manufacturing method of a high power super energy storage power supply. The manufacturing method is characterized in that the manufacturing method comprises the steps of: (1) preparing dielectric insulating material ink and inner electrode conducting material ink; (2) manufacturing a rapid 3D laminating print blank to generate a laminated chip; (3) drying the laminated chip; (4) carrying out the isostatic pressing on the laminated chip; (5) sintering the laminated chip; (6) chamfering the laminated chip; (7) manufacturing a terminal electrode of the laminated chip; and (8) testing and packaging the laminated chip.
Deep learning in estimating prevalence and systemic risk factors for diabetic retinopathy: a multi ethnic study
Venue: npj Digital Medicine
Year: 2019

In any community, the key to understanding the burden of a specific condition is to conduct an epidemiological study. The deep learning system (DLS) recently showed promising diagnostic performance for diabetic retinopathy (DR) This study aims to use DLS as the grading tool, instead of human assessors, to determine the prevalence and the systemic cardiovascular risk factors for DR on fundus photographs, in patients with diabetes. This is a multi ethnic (5 races) multi site (8 datasets from Singapore, USA, Hong Kong, China and Australia) cross sectional study involving 18,912 patients (n 93,293 images) We compared these results and the time taken for DR assessment by DLS versus 17 human assessors 10 retinal specialists/ophthalmologists and 7 professional graders) The estimation of DR prevalence between DLS and human assessors is comparable for any DR, referable DR and vision threatening DR (VTDR) (Human assessors: 15.9, 6.5% and 4.1% DLS: 16.1% 6.4% 3.7% Both assessment methods identified similar risk factors (with comparable AUCs) including younger age, longer diabetes duration, increased HbA1c and systolic blood pressure, for any DR, referable DR and VTDR (p 0.05) The total time taken for DLS to evaluate DR from 93,293 fundus photographs was ~1 month compared to 2 years for human assessors. In conclusion, the prevalence and systemic risk factors for DR in multi ethnic population could be determined accurately using a DLS, in significantly less time than human assessors. This study highlights the potential use of AI for future epidemiology or clinical trials for DR grading in the global communities.
Research Issues and Future Directions in Web Mining: A Survey
Year: 2020

This paper is a work on survey on the existing techniques of web mining and the issues related to it. The World Wide Web acts as an interactive and popular way to transfer information. Due to the enormous and diverse information on the web, the users cannot make use of the information very effectively and easily. Data mining concentrates on non trivial extraction of implicit previously unknown and potential useful information from the very large amount of data. Web mining is an application of data mining which has become an important area of research due to vast amount of World Wide Web services in recent years. The aim of this paper is to provide the past and current techniques in Web Mining. This paper also reports the summary of various techniques of web mining approached from the following angles like Feature Extraction,
Soins palliatifs pluridisciplinaires chez un malade en fin de vie Accompagnement d'un mourant et de son entourage
Year: 2006

sante) Nous citerons celle donnee par la SFAP Les soins palliatifs sont des soins actifs delivres dans une approche globale de la personne atteinte d'une maladie grave, evolutive ou terminale. L'objectif des soins palliatifs est de soulager les douleurs physiques et les autres symptomes, mais aussi de prendre en compte la souffrance psychologique, sociale et spirituelle. Les soins palliatifs et l'accompagnement sont interdisciplinaires. Ils s'adressent au malade en tant que personne, a sa famille et a ses proches, a domicile ou en institution. La formation et le soutien des soignants et des benevoles L e mouvement des soins palliatifs a pris son essor a partir des annees 1960 en Angleterre. Le Dr Cicely Saunders y a joue un role preponderant en developpant le concept de douleur globale (douleur physique mais aussi psychologique, sociale et spirituelle) et en promouvant l'emploi des antalgiques morphiniques. Le Dr Elisabeth Kubler Ross, psychiatre suisse travaillant aux Etats Unis a ete quant a elle pionniere dans l'etude des aspects psychologiques des malades en fin de vie. En France, la circulaire Laroque du 26 aout 1986 relative a l'organisation des soins et a l'accompagnement des malades en phase terminale a donne une impulsion considerable a l'essor des soins palliatifs, relayee en 1999 par la loi Kouchner qui garantit un droit d'acces aux soins palliatifs pour toute personne en fin de vie La circulaire du 19 fevrier 2002 a precise l'organisation des soins palliatifs aux cotes des unites de soins palliatifs (USI) est avancee la notion de demarche palliative dans tous les services et le concept de lits identifies, le developpement des prises en charge palliatives a domicile et des reseaux de soins palliatifs a domicile. En 2004, en France, d'apres les donnees du site Internet de la SFAP (Societe francaise de soins palliatifs) il existait 78 unites de soins palliatifs comprenant au total 772 lits (3 000 en Angleterre) ainsi que 225 equipes mobiles de soins palliatifs et 93 reseaux de soins palliatifs a domicile.
Automation of Tacit Knowledge Using Machine Learning
Venue: 2019 6th International Conference on Soft Computing Machine Intelligence (ISCMI)
Year: 2019

This paper presents an approach to capture tacit knowledge effortlessly and efficiently. It is a well known fact that at a work place, converting tacit knowledge into explicit knowledge is difficult and hence organizations lose critical information and best practices when a skilled employee leaves. Hence, regular capture of implicit data becomes critical. This paper focusses not only on a technique to acquire this tacit knowledge but also how to make it as seamless as possible so that it does not become cumbersome on the employee. This paper discusses about a bot, which captures data from the employee on a regular basis and transforms this data into tacit knowledge using text analytics. The bot also self learns about the employee's role and the work that the employee is currently working on. The bot tunes itself to ask the right set of questions to the employees. This approach is generic and can be customized and extended to fit to one's project need.
Association between Social Integration and Face Mask Use Behavior during the SARS CoV 2 Pandemic in Japan: Results from U CORONA Study
Venue: International journal of environmental research and public health
Year: 2021

Face mask use is a critical behavior to prevent the spread of SARS CoV 2. We aimed to evaluate the association between social integration and face mask use during the COVID 19 pandemic in a random sample of households in Utsunomiya City, Greater Tokyo, Japan. Data included 645 adults in the Utsunomiya COVID 19 seROprevalence Neighborhood Association (U CORONA) study, which was conducted after the first wave of the pandemic, between 14 June 2020 and 5 July 2020, in Utsunomiya City. Social integration before the pandemic was assessed by counting the number of social roles, based on the Cohen's social network index. Face mask use before and during the pandemic was assessed by questionnaire, and participants were categorized into consistent mask users, new users, and current non users. Multinomial logistic regression analysis was used to examine the association between lower social integration score and face mask use. To account for possible differential non response bias, non response weights were used. Of the 645 participants, 172 (26.7% were consistent mask users and 460 (71.3% were new users, while 13 (2.0% were current non users. Lower social integration level was positively associated with non users (RRR: 1.76, 95% CI: 1.10, 2.82) Social integration may be important to promote face mask use.
Enhancing the Detection of Criminal Organizations in Mexico using ML and NLP
Venue: 2020 International Joint Conference on Neural Networks (IJCNN)
Year: 2020

This paper relies on Machine Learning (ML) and supervised Natural Language Processing (NLP) to generate a geo referenced database on the violent presence of Mexican Criminal Organizations (MCOs) between 2000 2018. This application responds to the need for high quality data on criminal groups to inform academic and policy analysis in a context of intense violence such as Mexico. Powered by ML and NLP tools, this computational social science application processes a vast collection of news stories written in Spanish to track MCOs' violent presence. The unprecedented granularity of the data allows disaggregating daily municipal information for 10 main MCOs comprising more than 200 specific criminal cells.