Miracle-dz/UniFAR-FTUs huggingface dataset:
A Tale of Two Regulatory Regimes: Creation and Analysis of a Bilingual Privacy Policy Corpus

Over the past decade, researchers have started to explore the use of NLP to develop tools aimed at helping the public, vendors, and regulators analyze disclosures made in privacy policies. With the introduction of new privacy regulations, the language of privacy policies is also evolving, and disclosures made by the same organization are not always the same in different languages, especially when used to communicate with users who fall under different jurisdictions. This work explores the use of language technologies to capture and analyze these differences at scale. We introduce an annotation scheme designed to capture the nuances of two new landmark privacy regulations, namely the EU’s GDPR and California’s CCPA/CPRA. We then introduce the first bilingual corpus of mobile app privacy policies consisting of 64 privacy policies in English (292K words) and 91 privacy policies in German (478K words), respectively with manual annotations for 8K and 19K fine-grained data practices. The annotations are used to develop computational methods that can automatically extract “disclosures” from privacy policies. Analysis of a subset of 59 “semi-parallel” policies reveals differences that can be attributed to different regulatory regimes, suggesting that systematic analysis of policies using automated language technologies is indeed a worthwhile endeavor.
Valet: Rule-Based Information Extraction for Rapid Deployment

We present VALET, a framework for rule-based information extraction written in Python. VALET departs from legacy approaches predicated on cascading finite-state transducers, instead offering direct support for mixing heterogeneous information–lexical, orthographic, syntactic, corpus-analytic–in a succinct syntax that supports context-free idioms. We show how a handful of rules suffices to implement sophisticated matching, and describe a user interface that facilitates exploration for development and maintenance of rule sets. Arguing that rule-based information extraction is an important methodology early in the development cycle, we describe an experiment in which a VALET model is used to annotate examples for a machine learning extraction model. While learning to emulate the extraction rules, the resulting model generalizes them, recognizing valid extraction targets the rules failed to detect.
Phase II Study of Sequential Infusion of DLI and Cytokine Induced Killer Cells for Patients Relapsed after Sequential for

20 Seventy-four patients relapsed after allogeneic stem cell transplantation were enrolled in a 21 phase IIA study and treated with the sequential infusion of donor lymphocytes (DLI) followed by 22 cytokine induced killer (CIK) cells. Seventy-three patients were available for the intention to treat 23 analysis. At least one infusion of CIK cells was given to 59 patients, while 43 patients received the 24 complete cell therapy planned (58%). Overall, 12 patients (16%) developed acute GvHD (aGvHD, of 25 grade I-II in 7 cases and grade III-IV in 5). In 8 of 12 cases, aGvHD developed during DLI treatment 26 leading to interruption of the cellular program in 3 patients while in the remaining 5 cases aGvHD 27 was controlled by steroids treatment thus allowing the subsequent planned administration of CIK 28 cells. Chronic GvHD (cGvHD) was observed in 11 patients (15%). A complete response was 29 observed in 19 (26%), partial response in 3 (4%), stable disease in 8 (11%), early death in 2 (3%), 30 while progression of disease in the remaining 41 patients (56%). At 1 and 3 years, the progression 31 free survival was 31% and 29%, while the overall survival (OS) was 51% and 40%, respectively. By 32 multivariate analysis, the type of relapse, the presence of cGvHD and a short (<6 months) time from 33 alloHSCT to relapse were the significant predictors of survival. In conclusion, a low incidence of GvHD is observed after the sequential administration of DLI and CIK cells and disease control can 35 be achieved mostly after a cytogenetic or molecular relapse.
Improving Fairness in the Prediction of Heart Failure Length of Stay and Mortality by Integrating Social Determinants of Health

Background: Machine learning (ML) approaches have been broadly applied to the prediction of length of stay and mortality in hospitalized patients. ML may also reduce societal health burdens, assist in health resources planning and improve health outcomes. However, the fairness of these ML models across ethnoracial or socioeconomic subgroups is rarely assessed or discussed. In this study, we aim (1) to quantify the algorithmic bias of ML models when predicting the probability of long-term hospitalization or in-hospital mortality for different heart failure (HF) subpopulations, and (2) to propose a novel method that can improve the fairness of our models without compromising predictive power. Methods: We built 5 ML classifiers to predict the composite outcome of hospitalization length-of-stay and in-hospital mortality for 210 368 HF patients extracted from the Get With The Guidelines-Heart Failure registry data set. We integrated 15 social determinants of health variables, including the Social Deprivation Index and the Area Deprivation Index, into the feature space of ML models based on patients’ geographies to mitigate the algorithmic bias. Results: The best-performing random forest model demonstrated modest predictive power but selectively underdiagnosed underserved subpopulations, for example, female, Black, and socioeconomically disadvantaged patients. The integration of social determinants of health variables can significantly improve fairness without compromising model performance. Conclusions: We quantified algorithmic bias against underserved subpopulations in the prediction of the composite outcome for HF patients. We provide a potential direction to reduce disparities of ML-based predictive models by integrating social determinants of health variables. We urge fellow researchers to strongly consider ML fairness when developing predictive models for HF patients.
A Systematic Approach to Derive a Refined Speech Corpus for Sinhala

Speech Recognition is an active research area where advances of technology have continuously driven the development of research work. However, due to the lack of adequate resources, certain languages such as Sinhala, are left to underutilize the technology. With techniques such as crowdsourcing and web scraping, several Sinhala corpora have been created and made publicly available. Despite them being large and generic, the correctness and consistency in their text data remain questionable, especially due to the lack of uniformity in the language used in the different sources of web scraped text. Addressing that requires a thorough understanding of technical and linguistic particulars pertaining to the language, which often leaves the issue unattended. We have followed a systematic approach to derive a refined corpus using a publicly available corpus for Sinhala speech recognition. In particular, we standardized the transcriptions of the corpus by removing noise in the text. Further, we applied corrections based on Sinhala linguistics. A comparative experiment shows a promising effect of the linguistic corrections by having a relative reduction of the Word-Error-Rate by 15.9%.
Unifying Morphology Resources with OntoLex-Morph. A Case Study in German

The OntoLex vocabulary has become a widely used community standard for machine-readable lexical resources on the web. The primary motivation to use OntoLex in favor of tool- or application-specific formalisms is to facilitate interoperability and information integration across different resources. One of its extension that is currently being developed is a module for representing morphology, OntoLex-Morph. In this paper, we show how OntoLex-Morph can be used for the encoding and integration of different types of morphological resources on a unified basis. With German as the example, we demonstrate it for (a) a full-form dictionary with inflection information (Unimorph), (b) a dictionary of base forms and their derivations (UDer), (c) a dictionary of compounds (from GermaNet), and (d) lexicon and inflection rules of a finite-state parser/generator (SMOR/Morphisto). These data are converted to OntoLex-Morph, their linguistic information is consolidated and corresponding lexical entries are linked with each other.
A survey of spatial crowdsourcing

Crowdsourcing is a computing paradigm where humans are actively involved in a computing task, especially for tasks that are intrinsically easier for humans than for computers. Spatial crowdsourcing (SC) is an increasing popular category of crowdsourcing in the era of mobile Internet and sharing economy, where tasks are spatiotemporal and must be completed at a specific location and time. In fact, spatial crowdsourcing has stimulated a series of recent industrial successes including sharing economy for urban services (Uber and Gigwalk) and spatiotemporal data collection (OpenStreetMap and Waze). This survey dives deep into the challenges and techniques brought by the unique characteristics of spatial Y. Tong State Key Laboratory of Software Development Environment, Beijing Advanced Innovation Center for Big Data and Brain Computing and International Research Institute for Multidisciplinary Science, Beihang University, Beijing, China E-mail: yxtong@buaa.edu. cn Z. Zhou Computer Engineering and Networks Laboratory, ETH Zurich, Zurich, Switzerland E-mail: zzhou@tik.ee. ethz.ch Y. Zeng Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China E-mail: yzengal@cse.ust. hk L. Chen Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China E-mail: leichen@cse.ust. hk C. Shahabi Department of Computer Science, University of Southern California, California, USA E-mail: shahabi@usc.edu crowdsourcing. Particularly, we identify four core algorithmic issues in spatial crowdsourcing: (1) task assignment, (2) quality control, (3) incentive mechanism design and (4) privacy protection. We conduct a comprehensive and systematic review of existing research on the aforementioned four issues. We also analyze representative spatial crowdsourcing applications and explain how they are enabled by these four technical issues. Finally, we discuss open questions that need to be addressed for future spatial crowdsourcing research and applications.
Bitter melon juice concentrate improves depressive symptoms in a mouse model: Possible effect on brain cortisol

Depression is a prevalent mental disorder with detrimental impacts on quality of life and economy. Under the current COVID-19 pandemic, the incidence of depression has increased profoundly. To make matters worse, access to healthcare is challenged by the ongoing pandemic. This urges the availability of alternatives to treat depression with easy accessibility, and plant-derived substances can be good candidates. Bitter melon (Momordica charantia L.) has been known for its anti-inflammatory and antioxidant activities. We tested the effect of bitter melon juice concentrate (BM) in a mouse model of depression using the forced swimming test (FST) method. Male ddY mice were assigned to groups treated with daily oral vehicle, BM at 50 or 200 mg/kg, and 20 mg/kg fluoxetine hydrochloride. Immobility time during FST was observed on the 7th and 14th days of treatment. Upon completion of treatment, the mice were sacrificed and checked for hippocampal and frontal cortical cortisol levels. Results showed that BM produced a significant decrease in immobility time on both observation days. This finding was accompanied by a significant decline in cortisol levels in the brain areas of the mice treated with BM at both doses. Overall, results suggest that BM has the potential for use in the treatment of depression. © 2022 Anggadiredja and Garmana. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
Peatland Fire Photo Geotagging using Smartphones as an Investigative Tool

One of the ways to control tropical peat fires is to use a combination of the Global Navigation Satellite System (GNSS) and smartphones for monitoring, reporting, and verification of the location of the fires. The latest smartphones have many sensors, such as a compass, accelerometer, GPS, and a camera, to collect forest activity data from a specific target area. The collected data were then transferred to a cloud server through global mobile communication. This paper discusses a mobile and web application-based approach to collect and analyze user-generated geographical information of human activity data. The paper aims is to promote law enforcement agencies, local government, and fire patrol to consider the use of this low-cost, easy-to-use technology in controlling and reducing the risk of tropical peatland fires. Our mobile application has an easy-to-use mapping technology that allows its users to locate addresses quickly and provides cartographic maps augmented with digital information and high-resolution aerial imagery. This study proposes online citizen reporting as a new approach for law enforcement by aiding local government and fire patrols to conduct monitoring, reporting, and verification to reduce the risk of peat forest fires.
Complexity research in economics: past, present and future

paper for the in the the Abstract In this paper, we provide a brief overview of the field of complexity research in economics, and discuss directions of research that we consider to be promising in terms of solving open issues. We start the survey of the field with the research that emerged in the 1990s, when under the influence of earlier developments in the natural sciences (e.g., thermodynamics and chaos theory), the term complexity became in fashion to refer to theoretical ideas about how “ordered” patterns at an aggregate level can emerge from interaction between heterogenous agents at the microeconomic level. This gave rise to the notion of self-organization in dissipative systems, or “order at the edge of chaos” to describe economic dynamics. Because disequilibrium plays a large role in these theories, these ideas worked very well in combination with a Schumpeterian view of the economy, which also stresses disequilibrium. In the current literature, economic complexity is mainly used to refer to the application of quantitative methods based on networks that can be created on the basis of very fine-grained data on production or trade. These data are used to produce aggregate measures of development, as well as to describe how production may time. This literature developed largely disconnected to the earlier literature. The economic complexity paradigm is largely void of economic and instead aims to provide a set of data reduction techniques that are used to characterize there is to be the larger
Life of patients with cerebellar ataxia: A systematic review

Objectives: Conduct a literature search for scientific tests concerning treatment combined with well-being in cerebellar ataxias using electronic data sources. Methods: A search was conducted organized by scientific tests worried about the treatment allied to well-being in cerebellar ataxias using the electronic data sources PubMed, Medline, Embase, Cinahl and Pedro, and listings of recommendations of articles, from 1980 to December 2011, including in English and Dutch. Results: Information suggests that physical treatment, when included in occupational treatment, may improve international practical reputation, just as treatment at work alone may decrease signs of anxiety (grade 3). Conclusion: We have found some care for performing physical and work treatment, but other studies are necessary to create medical technique suggestions.
Advances in theoretical physics : Landau memorial conference : Chernogolokova, Russia, 22-26 June 2008

The conference 'Advances in Theoretical Physics', held in Chernogolovka, Russia, is devoted to Lev Landau's 100 year anniversary. It was organized by the Landau Institute for Theoretical Physics, which was established by Landau's pupils who carried on his traditions. The conference reviews current progress in the main branches of theoretical physics. The talks covered solid state physics and cosmology, low temperature physics and optics, quantum field theory and statistical physics, physics of low-dimensional systems and hydrodynamics.
Performance Evaluation of Scale-out NAS for HDFS

— Isilon of EMC Corporation, which is the major company of the storage production, announces the correspondence to Hadoop Distributed File System (HDFS). Hadoop is parallel distributed processing base for large-scale data constructed in HDFS and MapReduce. In addition, it can treat huge files using plural computers link in Hadoop. However, the tendency of detailed performance and various parameters is not known. Therefore, in this paper, we perform the comparison in the case with normal HDFS and Isilon for HDFS using a benchmark about writing performance and reading performance.
Influences of Meshing and High-Performance Computing towards Advancing the Numerical Analysis of High-Velocity Impacts

—By now, computers and software have spread into all fields of industry. The use of finite-difference and finite-element computer codes to solve problems involving fast, transient loading is commonplace. A large number of commercial codes exist and are applied to problems ranging from fairly low to extremely high damage levels. Therefore, extensive efforts are currently made in order to improve the safety by applying certain numerical solutions. For many engineering problems involving shock and impact, there is no single ideal numerical method that can reproduce the various aspects of a problem. An approach which combines different techniques in a single numerical analysis can provide the “best” solution in terms of accuracy and efficiency. But, what happens if code predictions do not correspond with reality? This paper discusses various factors related to the computational mesh that can lead to disagreement between computations and experience. Furthermore, the influence of high-performance computing is a main subject of this work. The goal is to find an appropriate technique for simulating composite materials and thereby improve modern armor to meet current challenges. Given the complexity of penetration processes, it is not surprising that the bulk of work in this area is experimental in nature. Terminal ballistic test techniques, aside from routine proof tests, vary mainly in the degree of instrumentation provided and hence the amount of data retrieved. Here, both the ballistic trials as well as the analytical methods will be discussed.
FSM based protocol for wearable and implantable wireless sensors for healthcare

— Due to increasing population, shortage of doctors and to lead a better quality of life it is necessary to design a healthcare application as early as possible. This paper proposed the design of finite state machine (FSM) based protocol model for wearable smart devices suitable for monitoring a patient heart based on heart sound. This work present the design, commands, fault tolerance capability, low cost and maintenance free protocol together with taking the advantage of wireless sensor networks (WSNs) which provides environment sensing and data processing capabilities at low cost. FSM is designed in such a way that the system could react to adverse condition and take action (example alert/send SMS to doctor in possibility of stroke). FSM can have a fault tolerance method by moving to an "Oblivion" state and start a new session from "Initial" state instead of blocking due to any undefined event. FSM can also be modified based on future requirement.
Transmission of Cryptosporidium spp. among human and animal local contact networks in sub-Saharan Africa: a multi-country study

Introduction: Cryptosporidiosis has been identified as one of the major causes of diarrhoea and diarrhoea-associated deaths in young children in sub-Saharan Africa. This study traces back Cryptosporidium positive children to their human and animal contacts to identify transmission networks. Methods: Stool samples were collected from children below five years with diarrhoea in Gabon, Ghana, Madagascar and Tanzania. Cryptosporidium positive and negative initial cases (IC) were followed to the community, where stool samples from households, neighbours and animal contacts were obtained. Samples were screened for Cryptosporidium spp. by immunochromatographic tests and by sequencing the 18S rRNA gene and further subtyped at the 60 kDa glycoprotein gene ( gp60 ). Transmission clusters were identified and risk ratios (RR) calculated. Results: In total, 1,363 paediatric ICs, 184 (13%) were diagnosed with Cryptosporidium spp. One hundred eight contact networks were sampled from Cryptosporidium positive and 68 from negative ICs. Identical gp 60 subtypes were detected amongst two or more contacts in 39 (36%) of the networks from positive ICs and in one (1%) from negative ICs. In comparison to Cryptosporidium negative ICs, positive ICs had an increased risk of having Cryptosporidium positive household members (RR = 3.6; 95%-confidence interval (CI): 1.7–7.5) or positive neighbouring children (RR = 2.9; 95%-CI: 1.6–5.1), but no increased risk of having positive animals (RR = 1.2; 95%-CI: 0.8–1.9) in their contact network. Conclusion: Cryptosporidiosis in rural sub-Saharan Africa is characterized by infection clusters among human contacts, to which zoonotic transmission appears to contribute only marginally.
Human Dendritic Cells Transduced with Herpes Simplex Virus Amplicons Encoding Human Immunodeficiency Virus Type 1 (HIV-1) gp120 Elicit Adaptive Immune Responses from Human Cells Engrafted into NOD/SCID Mice and Confer Partial Protection against HIV-1 Chal

Gorantla, Santhi; Santos, Kathlyn; Meyer, VaKara; Dewhurst, Stephen; Bowers, William J.; Federoff, Howard J.; Gendelman, Howard; and Poluektova, Larisa, "Human Dendritic Cells Transduced with Herpes Simplex Virus Amplicons Encoding Human Immunodeficiency Virus Type 1 (HIV-1) gp120 Elicit Adaptive Immune Responses from Human Cells Engrafted into NOD/SCID Mice and Confer Partial Protection against HIV-1 Challenge" (2005). Virology Papers. 103. https://digitalcommons.unl. edu/virologypub/103
Word Sense Disambiguation Based on Distance Metric Learning from Training Documents

—Word sense disambiguation task reduces to a classiﬁcation problem based on supervised learning. However, even though Support Vector Machine (SVM) gives the distance from the data point to the separating hyperplane, SVM is difﬁcult to measure the distance between labeled and unlabeled data points. In this paper, we propose a novel word sense disambiguation method based on a distance metric learning to ﬁnd the most similar sentence. To evaluate the efﬁciency of the method of word sense disambiguation using the distance metric learning such as Neighborhood Component Analysis and Large Margin Nearest Neighbor, we make some experiments to compare with the result of the SVM classiﬁcation. The results of the experiments show this method is effective for word sense disambiguation in comparison with SVM and one nearest neighbor. Moreover, the proposed method is effective for analyzing the relation between the input sentence and all senses of the target word if the target word has more than two senses.