nils-herrmann/clean_scirepeval huggingface dataset:
The NarrativeQA Reading Comprehension Challenge

Reading comprehension (RC)—in contrast to information retrieval—requires integrating information and reasoning about events, entities, and their relations across a full document. Question answering is conventionally used to assess RC ability, in both artificial agents and children learning to read. However, existing RC datasets and tasks are dominated by questions that can be solved by selecting answers using superficial information (e.g., local context similarity or global term frequency); they thus fail to test for the essential integrative aspect of RC. To encourage progress on deeper comprehension of language, we present a new dataset and set of tasks in which the reader must answer questions about stories by reading entire books or movie scripts. These tasks are designed so that successfully answering their questions requires understanding the underlying narrative rather than relying on shallow pattern matching or salience. We show that although humans solve the tasks easily, standard RC models struggle on the tasks presented here. We provide an analysis of the dataset and the challenges it presents.
Modeling User Consumption Sequences

We study sequences of consumption in which the same item may be consumed multiple times. We identify two macroscopic behavior patterns of repeated consumptions. First, in a given user’s lifetime, very few items live for a long time. Second, the last consumptions of an item exhibit growing inter-arrival gaps consistent with the notion of increasing boredom leading up to eventual abandonment. We then present what is to our knowledge the first holistic model of sequential repeated consumption, covering all observed aspects of this behavior. Our simple and purely combinatorial model includes no planted notion of lifetime distributions or user boredom; nonetheless, the model correctly predicts both of these phenomena. Further, we provide theoretical analysis of the behavior of the model confirming these phenomena. Additionally, the model quantitatively matches a number of microscopic phenomena across a broad range of datasets. Intriguingly, these findings suggest that the observation in a variety of domains of increasing user boredom leading to abandonment may be explained simply by probabilistic conditioning on an extinction event in a simple model, without resort to explanations based on complex human dynamics.
Biorepositories: Building better biobanks

data-rich samples are essential for future research. But obtaining and storing these samples is not as straightforward as many researchers think. A cross the world, freezers and cabinet shelves are full of human samples. Biobanks — collections of biological material set aside for research — vary tremendously in size, scope and focus. Samples can be collected from the general population, from patients who have had surgery or a biopsy and from people who have recently died. Some collections date back decades. The Aboriginal genome, for instance, was sequenced from a lock of hair originally given to British ethnolo-gist Alfred Cort Haddon in the 1920s; he criss-crossed the world gathering samples that are now housed at the University of Cambridge, UK. Most collections contain dried or frozen blood, but tissues such as eye, brain and nail are also held. Some biobanks address different questions from others: a population-based biobank that collects dried blood and health data may be used to determine the genetic risk factors for breast cancer, whereas a disease biobank that collects tumour samples might be used to reveal different molecular forms of breast cancer. The number of tissue samples in US banks alone was estimated at more than 300 million at the turn of the century and is increasing by 20 million a year, according to a report 1 from the research organization RAND Corporation in Santa Monica, California. Those numbers are probably an underestimate, says Allison Hubel, director of the Biopreservation Core Resource at the University of Minnesota in Minneapolis. But many scientists still say they cannot obtain enough samples. A 2011 survey 2 of more than 700 cancer researchers found that 47% had trouble finding samples of sufficient quality. Because of this, 81% have reported limiting the scope of their work, and 60% said they question the findings of their studies. Whereas researchers would once have inspected biological specimens under a microscope or measured only a handful of chemical constituents, or analytes, now they want to profile hundreds of molecules, including DNA, RNA, proteins and metabolites. The Larger biobanks have invested in automated storage and retrieval systems to track samples and ensure that they are maintained at a constant temperature.
Chronic stress, drug use, and vulnerability to addiction.

Stress is a well-known risk factor in the development of addiction and in addiction relapse vulnerability. A series of population-based and epidemiological studies have identified specific stressors and individual-level variables that are predictive of substance use and abuse. Preclinical research also shows that stress exposure enhances drug self-administration and reinstates drug seeking in drug-experienced animals. The deleterious effects of early life stress, child maltreatment, and accumulated adversity on alterations in the corticotropin releasing factor and hypothalamic-pituitary-adrenal axis (CRF/HPA), the extrahypothalamic CRF, the autonomic arousal, and the central noradrenergic systems are also presented. The effects of these alterations on the corticostriatal-limbic motivational, learning, and adaptation systems that include mesolimbic dopamine, glutamate, and gamma-amino-butyric acid (GABA) pathways are discussed as the underlying pathophysiology associated with stress-related risk of addiction. The effects of regular and chronic drug use on alterations in these stress and motivational systems are also reviewed, with specific attention to the impact of these adaptations on stress regulation, impulse control, and perpetuation of compulsive drug seeking and relapse susceptibility. Finally, research gaps in furthering our understanding of the association between stress and addiction are presented, with the hope that addressing these unanswered questions will significantly influence new prevention and treatment strategies to address vulnerability to addiction.
Discovery of Ranking Fraud for Mobile Apps

Ranking fraud in the mobile App market refers to fraudulent or deceptive activities which have a purpose of bumping up the Apps in the popularity list. Indeed, it becomes more and more frequent for App developers to use shady means, such as inflating their Apps' sales or posting phony App ratings, to commit ranking fraud. While the importance of preventing ranking fraud has been widely recognized, there is limited understanding and research in this area. To this end, in this paper, we provide a holistic view of ranking fraud and propose a ranking fraud detection system for mobile Apps. Specifically, we first propose to accurately locate the ranking fraud by mining the active periods, namely leading sessions, of mobile Apps. Such leading sessions can be leveraged for detecting the local anomaly instead of globalanomaly of App rankings. Furthermore, we investigate three types of evidences, i.e., ranking based evidences, rating based evidences and review based evidences, by modeling Apps' ranking, rating and review behaviors through statistical hypotheses tests. In addition, we propose an optimization based aggregation method to integrate all the evidences for fraud detection. Finally, we evaluate the proposed system with real-world App data collected from the iOS App Store for a long time period. In the experiments, we validate the effectiveness of the proposed system, and show the scalability of the detection algorithm as well as some regularity of ranking fraud activities.
The Carotid Revascularization Endarterectomy versus Stenting Trial (CREST): stenting versus carotid endarterectomy for carotid disease.

Carotid artery stenosis causes up to 10% of all ischemic strokes. Carotid endarterectomy (CEA) was introduced as a treatment to prevent stroke in the early 1950s. Carotid stenting (CAS) was introduced as a treatment to prevent stroke in 1994.



The Carotid Revascularization Endarterectomy versus Stenting Trial (CREST) is a randomized trial with blinded end point adjudication. Symptomatic and asymptomatic patients were randomized to CAS or CEA. The primary end point was the composite of any stroke, myocardial infarction, or death during the periprocedural period and ipsilateral stroke thereafter, up to 4 years.



There was no significant difference in the rates of the primary end point between CAS and CEA (7.2% versus 6.8%; hazard ratio, 1.11; 95% CI, 0.81 to 1.51; P=0.51). Symptomatic status and sex did not modify the treatment effect, but an interaction with age and treatment was detected (P=0.02). Outcomes were slightly better after CAS for patients aged <70 years and better after CEA for patients aged >70 years. The periprocedural end point did not differ for CAS and CEA, but there were differences in the components, CAS versus CEA (stroke 4.1% versus 2.3%, P=0.012; and myocardial infarction 1.1% versus 2.3%, P=0.032).



In CREST, CAS and CEA had similar short- and longer-term outcomes. During the periprocedural period, there was higher risk of stroke with CAS and higher risk of myocardial infarction with CEA.
Red wines and flavonoids diminish Staphylococcus aureus virulence with anti-biofilm and anti-hemolytic activities.

The emergence of antibiotic resistant Staphylococcus aureus presents a worldwide problem that requires non-antibiotic strategies. This study investigated the anti-biofilm and anti-hemolytic activities of four red wines and two white wines against three S. aureus strains. All red wines at 0.5-2% significantly inhibited S. aureus biofilm formation and hemolysis by S. aureus, whereas the two white wines had no effect. Furthermore, at these concentrations, red wines did not affect bacterial growth. Analyses of hemolysis and active component identification in red wines revealed that the anti-biofilm compounds and anti-hemolytic compounds largely responsible were tannic acid, trans-resveratrol, and several flavonoids. In addition, red wines attenuated S. aureus virulence in vivo in the nematode Caenorhabditis elegans, which is killed by S. aureus. These findings show that red wines and their compounds warrant further attention in antivirulence strategies against persistent S. aureus infection.
Semantic hashing

We show how to learn a deep graphical model of the word-count vectors obtained from a large set of documents. The values of the latent variables in the deepest layer are easy to infer and gi ve a much better representation of each document than Latent Sem antic Analysis. When the deepest layer is forced to use a small numb er of binary variables (e.g. 32), the graphical model performs “semantic hashing”: Documents are mapped to memory addresses in such a way that semantically similar documents are located at near by ddresses. Documents similar to a query document can then be fo und by simply accessing all the addresses that differ by only a fe w bits from the address of the query document. This way of extending the efficiency of hash-coding to approximate matching is much fa ster than locality sensitive hashing, which is the fastest curre nt method. By using semantic hashing to filter the documents given to TFID , we achieve higher accuracy than applying TF-IDF to the entir document set.
A Machine Learning Approach for an Indonesian-English Cross Language Question Answering System

We have built a CLQA (Cross Language Question Answering) system for a source language with limited data resources (e.g. Indonesian) using a machine learning approach. The CLQA system consists of four modules: question analyzer, keyword translator, passage retriever and answer finder. We used machine learning in two modules, the question classifier (part of the question analyzer) and the answer finder. In the question classifier, we classify the EAT (Expected Answer Type) of a question by using SVM (Support Vector Machine) method. Features for the classification module are basically the output of our shallow question parsing module. To improve the classification score, we use statistical information extracted from our Indonesian corpus. In the answer finder module, using an approach different from the common approach in which answer is located by matching the named entity of the word corpus with the EAT of question, we locate the answer by text chunking the word corpus. The features for the SVM based text chunking process consist of question features, word corpus features and similarity scores between the word corpus and the question keyword. In this way, we eliminate the named entity tagging process for the target document. As for the keyword translator module, we use an Indonesian-English dictionary to translate Indonesian keywords into English. We also use some simple patterns to transform some borrowed English words. The keywords are then combined in boolean queries in order to retrieve relevant passages using IDF scores. We first conducted an experiment using 2,837 questions (about 10% are used as the test data) obtained from 18 Indonesian college students. We next conducted a similar experiment using the NTCIR (NII Test Collection for IR Systems) 2005 CLQA task by translating the English questions into Indonesian. Compared to the Japanese-English and Chinese-English CLQA results in the NTCIR 2005, we found that our system is superior to others except for one system that uses a high data resource employing 3 dictionaries. Further, a rough comparison with two other Indonesian-English CLQA systems revealed that our system achieved higher accuracy score. key words: Cross Language Question Answering, Indonesian-English CLQA, limited resource language, machine learning
Learning to Learn from Noisy Labeled Data

Despite the success of deep neural networks (DNNs) in image classification tasks, the human-level performance relies on massive training data with high-quality manual annotations, which are expensive and time-consuming to collect. There exist many inexpensive data sources on the web, but they tend to contain inaccurate labels. Training on noisy labeled datasets causes performance degradation because DNNs can easily overfit to the label noise. To overcome this problem, we propose a noisetolerant training algorithm, where a meta-learning update is performed prior to conventional gradient update. The proposed meta-learning method simulates actual training by generating synthetic noisy labels, and train the model such that after one gradient update using each set of synthetic noisy labels, the model does not overfit to the specific noise. We conduct extensive experiments on the noisy CIFAR-10 dataset and the Clothing1M dataset. The results demonstrate the advantageous performance of the proposed method compared to several state-of-the-art baselines.
Combining Retrieval, Statistics, and Inference to Answer Elementary Science Questions

What capabilities are required for an AI system to pass standard 4th Grade Science Tests? Previous work has examined the use of Markov Logic Networks (MLNs) to represent the requisite background knowledge and interpret test questions, but did not improve upon an information retrieval (IR) baseline. In this paper, we describe an alternative approach that operates at three levels of representation and reasoning: information retrieval, corpus statistics, and simple inference over a semi-automatically constructed knowledge base, to achieve substantially improved results. We evaluate the methods on six years of unseen, unedited exam questions from the NY Regents Science Exam (using only non-diagram, multiple choice questions), and show that our overall system’s score is 71.3%, an improvement of 23.8% (absolute) over the MLN-based method described in previous work. We conclude with a detailed analysis, illustrating the complementary strengths of each method in the ensemble. Our datasets are being released to enable further research.
Discovering work prioritisation patterns from event logs

Business process improvement initiatives typically employ various process analysis techniques, including evidence-based analysis techniques such as process mining, to identify new ways to streamline current business processes. While plenty of process mining techniques have been proposed to extract insights about the way in which activities within processes are conducted, techniques to understand resource behaviour are limited. At the same time, an understanding of resources behaviour is critical to enable intelligent and effective resource management an important factor which can significantly impact overall process performance. The presence of detailed records kept by today’s organisations, including data about who, how, what, and when various activities were carried out by resources, open up the possibility for real behaviours of resources to be studied. This paper proposes an approach to analyse one aspect of resource behaviour: the manner in which a resource prioritises his/her work. The proposed approach has been formalised, implemented, and evaluated using a number of synthetic and real datasets.
Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data

We present a factorized hierarchical variational autoencoder, which learns disentangled and interpretable representations from sequential data without supervision. Specifically, we exploit the multi-scale nature of information in sequential data by formulating it explicitly within a factorized hierarchical graphical model that imposes sequence-dependent priors and sequence-independent priors to different sets of latent variables. The model is evaluated on two speech corpora to demonstrate, qualitatively, its ability to transform speakers or linguistic content by manipulating different sets of latent variables; and quantitatively, its ability to outperform an i-vector baseline for speaker verification and reduce the word error rate by as much as 35% in mismatched train/test scenarios for automatic speech recognition tasks.
BiliScreen: Smartphone-Based Scleral Jaundice Monitoring for Liver and Pancreatic Disorders

Pancreatic cancer has one of the worst survival rates amongst all forms of cancer because its symptoms manifest later into the progression of the disease. One of those symptoms is jaundice, the yellow discoloration of the skin and sclera due to the buildup of bilirubin in the blood. Jaundice is only recognizable to the naked eye in severe stages, but a ubiquitous test using computer vision and machine learning can detect milder forms of jaundice. We propose BiliScreen, a smartphone app that captures pictures of the eye and produces an estimate of a person's bilirubin level, even at levels normally undetectable by the human eye. We test two low-cost accessories that reduce the effects of external lighting: (1) a 3D-printed box that controls the eyes' exposure to light and (2) paper glasses with colored squares for calibration. In a 70-person clinical study, we found that BiliScreen with the box achieves a Pearson correlation coefficient of 0.89 and a mean error of -0.09 ± 2.76 mg/dl in predicting a person's bilirubin level. As a screening tool, BiliScreen identifies cases of concern with a sensitivity of 89.7% and a specificity of 96.8% with the box accessory.
Transfer Learning in Collaborative Filtering for Sparsity Reduction

Data sparsity is a major problem for collaborative filtering (CF) techniques in recommender systems, especially for new users and items. We observe that, while our target data are sparse for CF systems, related and relatively dense auxiliary data may already exist in some other more mature application domains. In this paper, we address the data sparsity problem in a target domain by transferring knowledge about both users and items from auxiliary data sources. We observe that in different domains the user feedbacks are often heterogeneous such as ratings vs. clicks. Our solution is to integrate both user and item knowledge in auxiliary data sources through a principled matrix-based transfer learning framework that takes into account the data heterogeneity. In particular, we discover the principle coordinates of both users and items in the auxiliary data matrices, and transfer them to the target domain in order to reduce the effect of data sparsity. We describe our method, which is known as coordinate system transfer or CST, and demonstrate its effectiveness in alleviating the data sparsity problem in collaborative filtering. We show that our proposed method can significantly outperform several state-of-the-art solutions for this problem.
Synthesis of Research on Homework Grade level has a dramatic influence on homework ' s effectiveness

Grade level has a dramatic influence on homework's effectiveness. I n the 1950 edition of the Kncyclo-pedia of Educational Research, H J Oito wrote, "compulsory homework does not result in suffi ciently improved academic accom plishments to justify retention" (Otto 1950, p 380) Eighteen years later, PR Wildman went a step further, stating that "whenever homework crowds out S<K ial experience, outdix>r recreation, and creative activities, and whenever it usurps time devoted to sleep, it is not meeting the basic needs of children and adolescents" (Wildman 1968, p 203) Today, it is hard to fathom how these educators could have arrived at such negative appraisals of homework. We must realize, however, that our present atlitude is a third renaissance in the belief ihat homework has ped agogical value When the 20th century began, the mind was viewed as a muscle that could benefit from men tal exercise, or memorization Since memorization could be done at home, homework was viewed as good During the 1940s, the emphasis in education shifted from drill to In the 1980s, homework has again come to the fore. A primary stimulus behind its reemergence was the report A Nation at Risk, problem solving. Thus, as Ottos re mark exemplifies, homework fell out of favor The launch of Sputnik by the Russians in the 1950s reversed this trend. The public worried that edu cation lacked rigor and left children unprepared for complex technolo gies Homework, it was believed, could accelerate knowledge acquisi tion. The 1960s witnessed yet another reversal, with homework viewed as a symptom of needless pressure on students Wildman expressed the concern of the time that too much emphasis on school would lead to the neglect of other areas of personal fulfillment In the 1980s, homework has again come to the fore A primary stimulus behind its reemergence was the report A Nation at Risk (NCEE 1983). Given the swings in public attitude, can research tell us if homework serves a useful purpose? Not surpris-NOVEMBER 19K9
E-PROCUREMENT AN EMERGING SUPPLY-CHAIN MANAGEMENT SYSTEM IN THE HOSPITALITY INDUSTRY : PERSPECTIVES OF HOTELIERS IN THE KUMASI METROPOLIS

s the hotel industry grows into a more technologyoriented industry, the need to understand and adopt eprocurement practices becomes more important. This research explores the adoption of e-procurement by the hotels in the Kumasi metropolis, focusing more on hoteliers’ perception of the concept of e-procurement and its importance to the hotel industry. The data was collected through survey from eleven selected hotels in Kumasi Metropolis. Results indicated that hoteliers are using e-procurement but not with the internet but through telephone. Benefits derived from the implementation of e-procurement are quality of goods and services, saving time and cost. However some costs which include buying and fixing of technical equipments, consultation fees for service providers, training of existing staff and recruitment of new staff will incur incorporating e-procurement. It was recommended that hoteliers need to ensure the needed systems and tools are in place for the operationalization of eprocurement.