nils-herrmann/clean_scirepeval huggingface dataset:
Machine Learning-based Software Testing: Towards a Classification Framework

Software Testing (ST) processes attempt to verify and validate the capability of a software system to meet its required attributes and functionality. As software systems become more complex, the need for automated software testing methods emerges. Machine Learning (ML) techniques have shown to be quite useful for this automation process. Various works have been presented in the junction of ML and ST areas. The lack of general guidelines for applying appropriate learning methods for software testing purposes is our major motivation in this current paper. In this paper, we introduce a classification framework which can help to systematically review research work in the ML and ST domains. The proposed framework dimensions are defined using major characteristics of existing software testing and machine learning methods. Our framework can be used to effectively construct a concrete set of guidelines for choosing the most appropriate learning method and applying it to a distinct stage of the software testing life-cycle for automation purposes.
Outdoor air pollution and asthma

Traffic and power generation are the main sources of urban air pollution. The idea that outdoor air pollution can cause exacerbations of pre-existing asthma is supported by an evidence base that has been accumulating for several decades, with several studies suggesting a contribution to new-onset asthma as well. In this Series paper, we discuss the effects of particulate matter (PM), gaseous pollutants (ozone, nitrogen dioxide, and sulphur dioxide), and mixed traffic-related air pollution. We focus on clinical studies, both epidemiological and experimental, published in the previous 5 years. From a mechanistic perspective, air pollutants probably cause oxidative injury to the airways, leading to inflammation, remodelling, and increased risk of sensitisation. Although several pollutants have been linked to new-onset asthma, the strength of the evidence is variable. We also discuss clinical implications, policy issues, and research gaps relevant to air pollution and asthma.
A Framework for Evaluating the Results of the SZZ Approach for Identifying Bug-Introducing Changes

The approach proposed by Śliwerski, Zimmermann, and Zeller (SZZ) for identifying bug-introducing changes is at the foundation of several research areas within the software engineering discipline. Despite the foundational role of SZZ, little effort has been made to evaluate its results. Such an evaluation is a challenging task because the ground truth is not readily available. By acknowledging such challenges, we propose a framework to evaluate the results of alternative SZZ implementations. The framework evaluates the following criteria: (1) the earliest bug appearance, (2) the future impact of changes, and (3) the realism of bug introduction. We use the proposed framework to evaluate five SZZ implementations using data from ten open source projects. We find that previously proposed improvements to SZZ tend to inflate the number of incorrectly identified bug-introducing changes. We also find that a single bug-introducing change may be blamed for introducing hundreds of future bugs. Furthermore, we find that SZZ implementations report that at least 46 percent of the bugs are caused by bug-introducing changes that are years apart from one another. Such results suggest that current SZZ implementations still lack mechanisms to accurately identify bug-introducing changes. Our proposed framework provides a systematic mean for evaluating the data that is generated by a given SZZ implementation.
Data Distillation for Controlling Specificity in Dialogue Generation

People speak at different levels of specificity in different  A conversational agent should have this ability and know when to be specific and when to be general. We propose an approach that gives a neural network–based conversational agent this ability. Our approach involves alternating between data distillation and model training : removing training examples that are closest to the responses most commonly produced by the model trained from the last round and then retrain the model on the remaining dataset. Dialogue generation models trained with different degrees of data distillation manifest different levels of specificity. We then train a reinforcement learning system for selecting among this pool of generation models, to choose the best level of specificity for a given input. Compared to the original generative model trained without distillation, the proposed system is capable of generating more interesting and higher-quality responses, in addition to appropriately adjusting specificity depending on the context. Our research constitutes a specific case of a broader approach involving training multiple subsystems from a single dataset distinguished by differences in a specific property one wishes to model. We show that from such a set of subsystems, one can use reinforcement learning to build a system that tailors its output to different input contexts at test time. Depending on their knowledge, interlocutors, mood, etc.
A Survey of Evolution in Predictive Models and Impacting Factors in Customer Churn

The information-based prediction models using machine learning techniques have gained massive popularity during the last few decades. Such models have been applied in a number of domains such as medical diagnosis, crime prediction, movies rating, etc. Similar is the trend in telecom industry where prediction models have been applied to predict the dissatisfied customers who are likely to change the service provider. Due to immense financial cost of customer churn in telecom, the companies from all over the world have analyzed various factors (such as call cost, call quality, customer service response time, etc.) using several learners such as decision trees, support vector machines, neural networks, probabilistic models such as Bayes, etc. This paper presents a detailed survey of models from 2000 to 2015 describing the datasets used in churn prediction, impacting features in those datasets and classifiers that are used to implement prediction model. A total of 48 studies related to churn prediction in telecom industry are discussed using 23 datasets (3 public and 20 private). Our survey aims to highlight the evolution of techniques from simple features/learners to more complex learners and feature engineering or sampling techniques. We also give an overview of the current challenges in churn prediction and suggest solutions to resolve them. This paper will allow researchers such as data analysts in general and telecom operators in particular to choose best suited techniques and features to prepare their churn prediction models.
Cracking Bank PINs by Playing Mastermind

The bank director was pretty upset noticing Joe, the system administrator, spending his spare time playing Mastermind, an old useless game of the 70ies. He had fought the instinct of telling him how to better spend his life, just limiting to look at him in disgust long enough to be certain to be noticed. No wonder when the next day the director fell on his chair astonished while reading, on the newspaper, about a huge digital fraud on the ATMs of his bank, with millions of Euros stolen by a team of hackers all around the world. The article mentioned how the hackers had ‘played with the bank computers just like playing Mastermind’, being able to disclose thousands of user PINs during the one-hour lunch break. That precise moment, a second before falling senseless, he understood the subtle smile on Joe’s face the day before, while training at his preferred game, Mastermind.
Sentiment Analysis with Incremental Human-in-the-Loop Learning and Lexical Resource Customization

The adjustment of probabilistic models for sentiment analysis to changes in language use and the perception of products can be realized via incremental learning techniques. We provide a free, open and GUI-based sentiment analysis tool that allows for a) relabeling predictions and/or adding labeled instances to retrain the weights of a given model, and b) customizing lexical resources to account for false positives and false negatives in sentiment lexicons. Our results show that incrementally updating a model with information from new and labeled instances can substantially increase accuracy. The provided solution can be particularly helpful for gradually refining or enhancing models in an easily accessible fashion while avoiding a) the costs for training a new model from scratch and b) the deterioration of prediction accuracy over time.
Automatic Detection of Verbal Deception

Detecting deception is an ancient problem that continues to find relevance today in many areas, such as border control, financial auditing, testimony assessment, and Internet fraud. Over the last century, research in this area has focused mainly on discovering physiological signals and psychological behaviors that are associated with lying. Using verbal cues (i.e., words and language structure) is not entirely new. But in recent years, data-driven and machine learning frameworks, which are now ubiquitous in the natural language processing (NLP) community, has brought new light to this old field. This highly accessible book puts last decade’s research in verbal deception in the context of traditional methods. It is a valuable resource to anyone with a basic understanding of machine learning looking to make inroads or break new ground in the subspecialty of detecting verbal deception. The book consists of five chapters organized into three parts—background on nonverbal cues, statistical NLP approaches, and future directions. The introductory chapter concisely defines the problem and relates verbal cues to the behavioral ones. It also provides an intuition of why patterns in language would be effective and provides an estimated state-of-the-art performance. Chapter 2 provides background on a behavioral approach to identifying deception. The first section gets readers acquainted with terms used by nonverbal cues to deception. These include physiological signals (which are the basis of well-known lie detection methods such as polygraphy and thermography), vocal cues (such as speech disfluencies), and body and facial expressions (e.g., pupil dilation). Although seemingly detached from the focus of the book, this preliminary material is an interesting introduction that also serves as a terminology reference later. The remaining chapter covers two topics: psychology of deception and applied criminal justice. The part on psychology presents a literature review of two definitive meta-analysis of the literature in the twentieth century. It first gives a theoretical account of deceptive behavior, such as motivation to lie and emotional states of liars. Next, it reports the experimental effectiveness of measurable cues, whether objectively or subjectively, such as complexity and amount of information. The second meta-analysis examines conditions that tend to make lying behavior more obvious, for example, interrogation. Although seemingly unrelated to NLP, I expect these reviews to be a source of inspiration for novel feature and model engineering. Because the material is very comprehensive and possibly foreign to the NLP community, I would like to see this part organized by the type of behavior cues (in the same vein as the preliminary material on physiological
Spontaneous Symmetry Breaking in Neural Networks

We propose a framework to understand the unprecedented performance and robustness of deep neural networks using field theory. Correlations between the weights within the same layer can be described by symmetries in that layer, and networks generalize better if such symmetries are broken to reduce the redundancies of the weights. Using a two parameter field theory, we find that the network can break such symmetries itself towards the end of training in a process commonly known in physics as spontaneous symmetry breaking. This corresponds to a network generalizing itself without any user input layers to break the symmetry, but by communication with adjacent layers. In the layer decoupling limit applicable to residual networks (He et al., 2015), we show that the remnant symmetries that survive the non-linear layers are spontaneously broken. The Lagrangian for the non-linear and weight layers together has striking similarities with the one in quantum field theory of a scalar. Using results from quantum field theory we show that our framework is able to explain many experimentally observed phenomena,, such as training on random labels with zero error (Zhang et al., 2017), the information bottleneck, the phase transition out of it and gradient variance explosion (Shwartz-Ziv & Tishby, 2017), shattered gradients (Balduzzi et al., 2017), and many more.
Modeling the Dynamics of Non-Player Characters' Social Relations in Video Games

Building credible Non-Playing Characters (NPCs) in games requires not only to enhance the graphic animation but also the behavioral model. This paper tackles the problem of the dynamics of NPCs social relations depending on their emotional interactions. First, we discuss the need for a dynamic model of social relations. Then, we present our model of social relations for NPCs and we give a qualitative model of the influence of emotions on social relations. We describe the implementation of this model and we briefly illustrate its features on a simple scene.
The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists

The DAVID Gene Functional Classification Tool http://david.abcc.ncifcrf.gov uses a novel agglomeration algorithm to condense a list of genes or associated biological terms into organized classes of related genes or biology, called biological modules. This organization is accomplished by mining the complex biological co-occurrences found in multiple sources of functional annotation. It is a powerful method to group functionally related genes and terms into a manageable number of biological modules for efficient interpretation of gene lists in a network context.
Read and Comprehend by Gated-Attention Reader with More Belief

Gated-Attention (GA) Reader has been effective for reading comprehension. GA Reader makes two assumptions: (1) a uni-directional attention that uses an input query to gate token encodings of a document; (2) encoding at the cloze position of an input query is considered for answer prediction. In this paper, we propose Collaborative Gating (CG) and Self-Belief Aggregation (SBA) to address the above assumptions respectively. In CG, we first use an input document to gate token encodings of an input query so that the influence of irrelevant query tokens may be reduced. Then the filtered query is used to gate token encodings of an document in a collaborative fashion. In SBA, we conjecture that query tokens other than the cloze token may be informative for answer prediction. We apply self-attention to link the cloze token with other tokens in a query so that the importance of query tokens with respect to the cloze position are weighted. Then their evidences are weighted, propagated and aggregated for better reading comprehension. Experiments show that our approaches advance the state-of-theart results in CNN, Daily Mail, and Who Did What public test sets.
A Probabilistic Multi-Touch Attribution Model for Online Advertising

It is an important problem in computational advertising to study the effects of different advertising channels upon user conversions, as advertisers can use the discoveries to plan or optimize advertising campaigns. In this paper, we propose a novel Probabilistic Multi-Touch Attribution (PMTA) model which takes into account not only which ads have been viewed or clicked by the user but also when each such interaction occurred. Borrowing the techniques from survival analysis, we use the Weibull distribution to describe the observed conversion delay and use the hazard rate of conversion to measure the influence of an ad exposure. It has been shown by extensive experiments on a large real-world dataset that our proposed model is superior to state-of-the-art methods in both conversion prediction and attribution analysis. Furthermore, a surprising research finding obtained from this dataset is that search ads are often not the root cause of final conversions but just the consequence of previously viewed ads.
The anatomy and embryology of posterior urethral valves.

We reviewed the literature to better elucidate the history behind our understanding of the anatomy, classification and embryology of posterior urethral valves.



A directed MEDLINE literature review of the anatomy, classification and embryology of posterior urethral valves was performed. An effort was made to focus on the most frequently cited historical articles as well as those including detailed anatomical analyses of fetal specimens. Also included was the analysis of a specimen obtained at our institution in a novel manner that to our knowledge has not been previously described in the literature with respect to the anatomy of posterior urethral valves.



The precise origins regarding the anatomy and embryology of posterior urethral valves remain undefined. However, the literature is abundant in theories regarding the origin of posterior urethral valves, based primarily on small uncontrolled series or case reports. There are a limited number of reports of the anatomy of posterior urethral valves in methodical fashion using reproducible scientific techniques such as histopathology. These reports are invaluable for providing a foundation of how to properly study and define the origins of posterior urethral valves.



Elucidating this most fundamental feature of a congenital condition central to the practice of pediatric urology is essential. More well designed studies specifically with this goal in mind are necessary. Incorporating new reconstructive imaging modalities may assist us in pinpointing the elusive origins of the embryology and anatomy of posterior urethral valves.
On Improving the Accuracy of Readability Classification using Insights from Second Language Acquisition

We investigate the problem of readability assessment using a range of lexical and syntactic features and study their impact on predicting the grade level of texts. As empirical basis, we combined two web-based text sources, Weekly Reader and BBC Bitesize, targeting different age groups, to cover a broad range of school grades. On the conceptual side, we explore the use of lexical and syntactic measures originally designed to measure language development in the production of second language learners. We show that the developmental measures from Second Language Acquisition (SLA) research when combined with traditional readability features such as word length and sentence length provide a good indication of text readability across different grades. The resulting classifiers significantly outperform the previous approaches on readability classification, reaching a classification accuracy of 93.3%.
Fast Context Adaptation via Meta-Learning

We propose CAVIA, a meta-learning method for fast adaptation that is scalable, flexible, and easy to implement. CAVIA partitions the model parameters into two parts: context parameters that serve as additional input to the model and are adapted on individual tasks, and shared parameters that are meta-trained and shared across tasks. At test time, the context parameters are updated with one or several gradient steps on a task-specific loss that is backpropagated through the shared part of the network. Compared to approaches that adjust all parameters on a new task (e.g., MAML), CAVIA can be scaled up to larger networks without overfitting on a single task, is easier to implement, and is more robust to the inner-loop learning rate. We show empirically that CAVIA outperforms MAML on regression, classification, and reinforcement learning problems.
Dental microwear and diets of African early Homo.

Conventional wisdom ties the origin and early evolution of the genus Homo to environmental changes that occurred near the end of the Pliocene. The basic idea is that changing habitats led to new diets emphasizing savanna resources, such as herd mammals or underground storage organs. Fossil teeth provide the most direct evidence available for evaluating this theory. In this paper, we present a comprehensive study of dental microwear in Plio-Pleistocene Homo from Africa. We examined all available cheek teeth from Ethiopia, Kenya, Tanzania, Malawi, and South Africa and found 18 that preserved antemortem microwear. Microwear features were measured and compared for these specimens and a baseline series of five extant primate species (Cebus apella, Gorilla gorilla, Lophocebus albigena, Pan troglodytes, and Papio ursinus) and two protohistoric human foraging groups (Aleut and Arikara) with documented differences in diet and subsistence strategies. Results confirmed that dental microwear reflects diet, such that hard-object specialists tend to have more large microwear pits, whereas tough food eaters usually have more striations and smaller microwear features. Early Homo specimens clustered with baseline groups that do not prefer fracture resistant foods. Still, Homo erectus and individuals from Swartkrans Member 1 had more small pits than Homo habilis and specimens from Sterkfontein Member 5C. These results suggest that none of the early Homo groups specialized on very hard or tough foods, but that H. erectus and Swartkrans Member 1 individuals ate, at least occasionally, more brittle or tough items than other fossil hominins studied.
Malaria: Global progress 2000 – 2015 and future challenges

2015 was the target year for malaria goals set by the World Health Assembly and other international institutions to reduce malaria incidence and mortality. A review of progress indicates that malaria programme financing and coverage have been transformed since the beginning of the millennium, and have contributed to substantial reductions in the burden of disease.



Investments in malaria programmes increased by more than 2.5 times between 2005 and 2014 from US$ 960 million to US$ 2.5 billion, allowing an expansion in malaria prevention, diagnostic testing and treatment programmes. In 2015 more than half of the population of sub-Saharan Africa slept under insecticide-treated mosquito nets, compared to just 2 % in 2000. Increased availability of rapid diagnostic tests and antimalarial medicines has allowed many more people to access timely and appropriate treatment. Malaria incidence rates have decreased by 37 % globally and mortality rates by 60 % since 2000. It is estimated that 70 % of the reductions in numbers of cases in sub-Saharan Africa can be attributed to malaria interventions.



Reductions in malaria incidence and mortality rates have been made in every WHO region and almost every country. However, decreases in malaria case incidence and mortality rates were slowest in countries that had the largest numbers of malaria cases and deaths in 2000; reductions in incidence need to be greatly accelerated in these countries to achieve future malaria targets. Progress is made challenging because malaria is concentrated in countries and areas with the least resourced health systems and the least ability to pay for system improvements. Malaria interventions are nevertheless highly cost-effective and have not only led to significant reductions in the incidence of the disease but are estimated to have saved about US$ 900 million in malaria case management costs to public providers in sub-Saharan Africa between 2000 and 2014. Investments in malaria programmes can not only reduce malaria morbidity and mortality, thereby contributing to the health targets of the Sustainable Development Goals, but they can also transform the well-being and livelihood of some of the poorest communities across the globe.